Device-Free Passive Identity Identification via WiFi Signals

Transcription

1 enor Article Device-Free Paive Identity Identification via WiFi Signal Jiguang Lv ID, Wu Yang * and Dapeng Man Information Security Reearch Center, Harbin Engineering Univerity, Harbin 5, China; lvjiguang@hrbeu.edu.cn (J.L.); mandapeng@hrbeu.edu.cn (D.M.) * Correpondence: yangwu@hrbeu.edu.cn; Tel.: Received: 6 September 7; Accepted: 7 October 7; Publihed: November 7 Abtract: Device-free paive identity identification attract much attention in recent year, and it i a repreentative application in enorle ening. It can be ued in many application uch a intruion detection and mart building. Previou tudie how ening potential WiFi ignal in a device-free paive manner. It i confirmed that human gait i unique from each or imilar to fingerprint and iri. However, identification accuracy exiting approache i not atifactory in practice. In thi paper, we preent Wii, a device-free WiFi-baed Identity Identification approach utilizing human gait baed on Channel State Information (CSI) WiFi ignal. Principle Component Analyi (PCA) and low pa filter are applied to remove noie in ignal. We n extract everal entitie gait feature from both time and frequency domain, and elect mot effective feature according to information gain. Baed on e feature, Wii realize tranger recognition through Gauian Mixture Model (GMM) and identity identification through a Support Vector Machine (SVM) with Radial Bai Function (RBF) kernel. It i implemented uing commercial WiFi device and evaluated on a dataet with more than 5 gait intance collected from eight ubject walking in a room. The reult indicate that Wii can effectively recognize tranger and can achieve high identification accuracy with low computational cot. A a reult, Wii ha potential to work in typical home ecurity ytem. Keyword: WiFi; channel tate information; human gait; human identification. Introduction Identity identification ha been reearched for many year and re have been everal method for human identification uch a fingerprint-baed [], face recognition-baed [] and iri-baed [3] method. It i widely ued in ecurity ytem a e biological characteritic are unique among people and can provide high identification accuracy. The characteritic can be utilized in ecurity ytem to make ure wher omeone ha acce to a certain room. However, mot identity identification method eir need wearable device or participating actively in identification proce, which hinder ir applicability in ome pecific cenario uch a intruion detection. WLAN-baed device-free paive ening attract much attention in recent year becaue it doe not require uer to be equipped with any ening device or behave actively in ening proce [4]. WiFi ha been widely deployed in our daily life that it can provide Internet acce for a large number device. Numerou reearcher find that WiFi ignal can be utilized in device-free ening a WiFi device can not only be ued for communication, but alo act a generalized enor. The movement human ha an impact on ignal tranmiion, and different people gait may caue different multipath to ignal propagation. It i confirmed that human gait i different from each or and it can be een a hi unique characteritic. Thi motivate our reearch on WLAN-baed device-free identity identification. Compared to traditional ecurity ytem, which are baed on video Senor 7, 7, 5; doi:.339/75

2 Senor 7, 7, 5 7 camera or infrared equipment, WLAN-baed approache do not have diadvantage privacy leakage [5] and high cot. There have been everal pioneering tudie on WLAN-baed device-free identity identification [6 8]. Thee approache leverage Channel State Information (CSI) in phyical layer wirele network to model human gait. However, re are till many challenge in WiFi-baed human identification. One bigget challenge i how to extract effective gait pattern uing CSI dynamic. The received CSI waveform contain ignal reflection whole body. A a reult, it i difficult to divide CSI time erie into certain egment according to walking tep. In addition, CSI feature extraction i alo a challenging tak for human identification. In or word, we hould find proper feature to characterize human gait. The common feature uch a ignal variance, median value, and mean value are not effective becaue y can be eaily influenced by environmental change. Thu, re i a huge gap between extracted feature and exact gait character. To deal with e challenge, in thi paper, we preent Wii, a device-free WiFi-baed Identity Identification approach. It i compoed four main module, which are pre-proceing, tep egmentation, feature extraction and claification. Claification contain two ub-module, tranger recognition and identity identification. Firt, PCA and low pa filter are utilized to eliminate uele ignal component and high frequency noie. Then, walking tep are automatically egmented by Continuou Wavelet Tranform (CWT) and wavelet variance. Thirdly, everal time and frequency domain feature are extracted before a few m are elected according to information gain to repreent human gait. Finally, a Gauian Mixture Model (GMM) i utilized to recognize tranger and a Support Vector Machine (SVM) with Radial Bai Function (RBF) kernel act a claifier to identify people. We implement a prototype ytem in a 5 m 4 m meeting room with eight volunteer for evaluation and compare performance with WiWho and FreeSene. The reult how that tranger recognition rate can achieve over 9% and identity identification accuracy can achieve 9.9% to 98.7% when ize candidate uer et change from 8 to, which i feaible in common indoor cenario. In ummary, main contribution paper are a follow. We propoe Wii, a novel device-free WiFi-baed identity identification approach, which i capable recognizing tranger who are outide training et and ha a higher human identification accuracy compared to exiting method. We ue PCA and low pa filter to remove uele component in WiFi ignal and reduce computational complexity at ame time. We combine CWT and wavelet variance to egment CSI waveform into ingle walking tep. It i baed on obervation that wavelet coefficient how obviou periodicity. We extract and elect gait feature from both time and frequency domain according to information gain that can better repreent human walking pattern and reduce computational cot. A a reult, identification accuracy become higher. The ret thi paper i organized a follow. Section preent related work in human identification and device-free ening. The baic background knowledge CSI WiFi network i introduced in Section 3. In Section 4, we preent detail deign Wii, and experimental evaluation are preented in Section 5. Then we dicu potential and limitation Wii in Section 6. Finally, we conclude our work in Section 7.. Related Work A broad range identity identification approache have been propoed e year that can atify different cenario. Mot identity identification approache leverage biometric characteritic uch a fingerprint, iri and even gait. Iri i widely ued in uer auntication a it i unique among different people

3 Senor 7, 7, and very table acro different time. An iri recognition ytem i propoed in [9] including two module, localizing iri and iri pattern recognition. A digital camera i ued to capture image, from which iri i extracted. The iri i n recontructed into rectangle format and iri pattern i recognized. Park et al. propoe an iri recognition approach baed on core level fuion, in which two Gabor wavelet filter and SVM are ued [3]. Daugman Rubber Sheet Model i propoed to extract feature from iri including freckle, corona and tripe []. Fingerprint i alo widely ued to identify a peron, a it ha high uniquene. In fingerprint-baed uer auntication, ome pecial point called minutiae point are extracted from fingerprint image. The ridge thinning algorithm i put forward to extract minutiae feature from fingerprint []. Although recognition accuracy above technique i relative high, y all require pecific infratructure, which limit ir applicability. Recently, many reearcher pay ir attention on paive recognition cheme uing wirele ening, epecially WLAN-baed framework. There are many application in WLAN-baed wirele ening a it doe not require uer to carry any device and it can even only ue WiFi infratructure to achieve high accuracy ening uch a human detection, activity recognition and identity recognition. Human detection and crowd counting are fundamental procee indoor localization and identity recognition. Reearcher firt implement human detection ytem utilizing received ignal trength (RSS), a it i eay to obtain from many kind wirele device. Human preence can affect propagation wirele ignal and hence received ignal trength varie a people move [ 5]. However, RSS i a coare-grained meaurement that it ha weak tability due to evere multipath effect in indoor cenario. More recently, reearcher find channel tate information ha better propertie in phyical layer. A illutrated in [6], CSI i a more fine-grained meaurement wirele ignal compared with RSS a it i a ubcarrier level meaurement and contain both amplitude and phae, and it can be extracted from commodity device with light firmware modification. A a reult, CSI-baed paive human detection approache attract much attention. Mot CSI-baed method treat CSI a extended RSS, uch a [7]; variance CSI amplitude i extracted a feature to detect human motion. PADS extract feature from both amplitude and phae CSI to make human detection ytem more enitive and it can detect human different moving peed [8]. A proper feature extracted only from amplitude CSI can alo be enitive enough to detect human even if moving peed i very low [9]. Wirele ignal can be ued to provide a rough etimation number people in monitoring area. Different number people uually generate different ignal meaurement due to multipath effect in indoor cenario [ 3]. Device-free paive ening can alo be ued in activity recognition. E-eye can recognize a few daily activitie uch a leeping and cooking uing CSI hitogram a feature [4]. CARM i a model-baed activity recognition ytem [5]. It model relationhip between CSI and human activitie. WiDraw can track activity a peron hand according to Angle Arrival (AoA) WiFi ignal [6]. Smokey can detect if a peron I moking uing WiFi ignal even under NLOS environment [7]. WiFinger i preented to recognize minute variation ignal when finger move [8]. It i confirmed that gait i a unique feature a peron, a a reult which it can be ued to identify a ubject [9]. WiFi-ID extract repreentative feature people walking tyle and how for firt time that WiFi ignal can be ued to identify a peron [8]. WiWho i a framework uing WiFi ignal to identify a peron tep and walking gait [6]. It how that identity can be identified baed on tep and walk analyi. WifiU prile human movement in which pectrogram are generated from CSI [3]. FreeSene combine PCA, Dicrete Wavelet Tranform (DWT) and Dynamic Time Warping (DTW) technique to implement human identification [7]. Different from approache above, we for firt time calculate periodicity from CSI WiFi ignal uing Continuou Wavelet Tranform (CWT), and extract time and frequency domain feature from a ingle tep a well a a few tep. A a reult, identification accuracy Wii can be higher than above approache. Furrmore, Wii can recognize tranger from a group people.

4 Senor 7, 7, Preliminary Wii utilize CSI in phyical layer WiFi ignal. Conequently, in thi ection, we give a brief introduction CSI. In a common indoor cenario, wirele ignal propagate through multiple path to receiver. There may exit LOS path and everal reflection path, and received ignal i uperpoition ignal from different path. In OFDM ytem, wirele channel in time domain can be decripted by a Channel Impule Repone (CIR) to ditinguih different path. Under aumption time-invariant, CIR can be expreed a: h(τ) = N α i e jθ iδ(τ τ i ) + n(τ), () i= where α i, θ i, and τ i denote amplitude, phae and time delay ignal from ith path, repectively; N i total number path; n(τ) i complex Gauian white noie; and δ(τ) i Dirac delta function. However, precie CIR cannot be extracted from ordinary commodity infratructure which i inapplicable in home environment. To overcome thi limitation, in frequency domain, Channel Frequency Repone (CFR) can model tranmitting channel, which i compoed amplitude-frequency repone and phae-frequency repone. Given infinite bandwidth, CIR i equivalent to CFR, and CFR can be derived by taking Fat Fourier Tranform (FFT) CIR: H = FFT(h(τ)). () The Linux kernel driver Intel 53 NIC can be lightly modified to make it convenient to obtain CFR for N = 3 ubcarrier in format CSI: H = [H( f ), H( f ),..., H( f N )]. (3) The amplitude and phae a ubcarrier can be decribed by CSI: H( f k ) = H( f k ) e j in( H), (4) where H( f k ) i CSI ubcarrier which central frequency i f k, and H denote it phae. Therefore a group CSI H( f k ), (k =,..., K), reveal K ampled CFR at granularity ubcarrier level. 4. Sytem Deign In thi ection, overview Wii i preented followed by deign detail each module. 4.. Sytem Overview The framework Wii i provided in ; it ha four main component: pre-proceing, tep egmentation, feature extraction and claification. In claification procedure, it contain tranger recognition and identity identification before pot-proceing. Noie removing i conducted in preproceing a re are more than one kind noie included in wirele ignal. Step egmentation proce can divide continuou CSI data into dicrete egment according to walking tep in order to extract per-tep feature. In feature extraction module, everal time and frequency domain feature are extracted and feature that generate larget information gain are elected. Finally, tranger i recognized and identity i identified in claification module. The ytem work in a typical indoor environment with a pair commodity WiFi device deployed. A wirele router that upport IEEE 8.n protocol act a TX and a laptop equipped with ome certain model wirele network interface card (uch a Aro 939 and Intel 53) act

5 Senor 7, 7, a RX a well a erver. They keep tranmitting data to collect CSI when a peron walk in monitoring area. The collected CSI are tored in laptop, and will be proceed in Senor 7, 7, x 5 8 ubequent procedure. CSI Pre-proceing Step Segmentation Feature Extraction Stranger Recognition Identity Identification Pot-proceing Claification Laptop Wirele router.. Sytem framework Pre-Proceing During During our our experiment, we find that number collectedcsi CSIi inot not ame ame a a number number tranmitted ICMP packet weet. et. Thu, Thu, we we firt firt conduct conduct linear linear interpolation interpolation in raw rawcollected CSI CSI to calibrate to calibrate ampling ampling frequency. frequency. A a reult, A a reult, collected CSI collected have a CSI unified have a unified ampling ampling frequency frequency that we that can we n can n extract extract frequency frequency domain domain feature feature after after interpolation. interpolation. According to ory Orthogonal Frequency Diviion Multiplexing (OFDM), ubcarrier According to ory Orthogonal Frequency Diviion Multiplexing (OFDM), ubcarrier channel are independent and have different frequency which caue frequency elective fading channel are independent and have different frequency which caue frequency elective fading [3]. [3]. However, actually, CSI data adjacent ubcarrier have ome relationhip. For each However, actually, CSI data adjacent ubcarrier have ome relationhip. For each ICMP packet, ICMP packet, a CSI matrix 3 3 can be extracted. Conequently, we ue PCA to obtain a CSI matrix 3 3 can be extracted. Conequently, we ue PCA to obtain independent data. independent data. PCA can automatically combine correlated CSI tream to extract more PCArepreentative can automatically component. combine The CSI correlated matrix i CSI rehaped treaminto to extract a 9 more vector. repreentative It conit component. a n 9 Thematrix CSI matrix for n i collected rehaped CSI into vector a in 9a vector. time interval. It conit We calculate a n 9 matrix principle for ncomponent collected CSI vector in amatrix time interval. and find We that, calculate in mot cae, principle contribution component rate firt matrix principle and find component that, in i mot higher cae, than contribution 85%, thu rate we ue firt firt principle component i a higher repreentation than 85%, thu walking we ue data. firt principle component However, a re repreentation are everal kind walking noie data. reerved in firt principle component that have negative However, effect re on are everal identification kind accuracy. noie reerved The one inthat ha firt principle mot ignificant component negative that have negative influence effect i high on frequency identification noie due accuracy. to or The movement. one that ha The ignal mot component ignificant that negative reflected influence by i high toro, frequency arm and noie leg are dueueful to or in identifying movement. identity. The ignal The frequency component e that reflected component by toro, i lower arm andthan leg are Hz ueful according identifying to intuition identity. and our Theobervation. frequency Conequently, e component we utilize i a lower pa thanfilter Hz according with tocutf intuition frequency and our Hz obervation. to eliminate Conequently, high frequency we utilize noie a low in pa walking filter with data. After cutf frequency pre-proceing, Hz mot to eliminate noie in raw high CSI frequency ha been noie removed. in walking data. After pre-proceing, mot noie in raw CSI ha been removed Step Segmentation 4.3. StepOur Segmentation identity identification ytem i baed on human gait information, o it i neceary to find tart and end point each tep from collected data to divide data equence into Our identity identification ytem i baed on human gait information, o it i neceary to find egment according to ir walking tep. tart and end point each tep from collected data to divide data equence into egment Generally, a peron walking peed i contant in a hort time and refore, re i an according to ir walking tep. obviou periodicity during walking period. However, it i challenging uing wirele ignal to Generally, a peron walking peed i contant in a hort time and refore, re i an obviou determine periodicity walking. Unlike device-baed method uch a inertial periodicity enor-baed during ytem, walking in which period. enor However, i attached it i challenging to human body uing and wirele it i eay ignal to determine to determine periodicity. We walking. cannot oberve Unlike an obviou device-baed periodicity method directly uch from a inertial CSI waveform. enor-baed A a reult, ytem, we need to dig deep down into different frequency component uing time frequency analyi technique to find periodicity pattern. Fortunately, combination Continuou Wavelet

6 Senor 7, 7, in which enor i attached to human body and it i eay to determine periodicity. We cannot oberve Senor an 7, obviou 7, x periodicity directly from CSI waveform. A a reult, we need to dig deep 6 down 8 into different frequency component uing time-frequency analyi technique to find periodicity pattern. Tranform Fortunately, (CWT) and combination wavelet variance Continuou can effectively Wavelet dicover Tranform periodicity (CWT) in a and multi-cale wavelet manner variance canfrom effectively waveform. dicover periodicity in a multi-cale manner from waveform. Firt, Firt, we we calculate wavelet coefficient firt PCA component CSI CSI waveform waveform after after low-pa low-pa filtering filtering (cpl) (cpl) in in multiple cale uing CWT. According to toour ourexperimental reult reult trying trying different different wavelet wavelet function, function, we we chooe chooe DB6 DB6 (Daubechie) (Daubechie) wavelet wavelet [3]. [3]. A A hown hown in in,, we can ee clearly periodicity wavelet coefficient exit under ome cale. However, we can ee clearly periodicity wavelet coefficient exit under ome cale. However, exact periodicity cannot be confirmed intuitively from wavelet coefficient. The wavelet exact periodicity cannot be confirmed intuitively from wavelet coefficient. The wavelet variance reflect ditribution power wavelet coefficient in different cale, and it variance reflect ditribution power wavelet coefficient in different cale, and it can can be ued to etimate main periodicity a time erie [33]. Thu, we n calculate be ued to etimate main periodicity a time erie [33]. Thu, we n calculate wavelet wavelet variance a Equation (5). variance a Equation (5). + + var(a) var( = a) = ò WW f (a, ( a,b b) ) db f db,, (5) (5) where W f (a, b) where W ( a,b ) i i power power f wavelet coefficient cale a at at time time b. b. -. Wavelet coefficient multiple cale channel tate information.. Wavelet coefficient multiple cale channel tate information. Second, a indicated in 3, we can ee that maximum wavelet variance locate at Second, a indicated in 3, we can ee that maximum wavelet variance locate at cale about 9, which mean wavelet coefficient have mot obviou periodicity at thi cale. cale about 9, which mean wavelet coefficient have mot obviou periodicity at thi Baed cale. on Baed obervation on obervation our experiment, our experiment, thi periodicity thi periodicity i mainly caued i mainly by caued movement by toro, movement a toro toro, affect a toro ignal affect tranmiion ignal mot tranmiion ignificantly. mot ignificantly. It i noteworthy that location maximum wavelet variance varie among different people and even different time ame peron a walking peed i not ame among different people and not contant during walking. Fortunately, Wii i a window-baed approach, and it can calculate periodicity dynamically when people walk at definition a certain time window. A a reult, Wii ha ability to adapt to different walking peed a peron. Third, we can ue thi cale to extract correponding cpl that i relative to walking. When collecting CSI data, we alo ue a digital camera to take video record walking. Then, we compare waveform cpl thi cale (cpl ) with video record and find that waveform between two zero croing point can repreent one tep a hown in 4. However, ome noie in waveform exit; that i, time interval between two adjacent zero croing point i too hort or too long. Thu, we et lower bound t lb a.5 and upper bound t ub a. intuitively a time interval mot tep are between e two bound. 3. Wavelet variance.

7 . Wavelet coefficient multiple cale channel tate information. Second, a indicated in 3, we can ee that maximum wavelet variance locate at cale about 9, which mean wavelet coefficient have mot obviou periodicity at thi Senor cale. 7, Baed 7, 5on obervation our experiment, thi periodicity i mainly caued by 7 7 movement toro, a toro affect ignal tranmiion mot ignificantly. Senor 7, 7, x 7 8 It i noteworthy that location maximum wavelet variance varie among different people and even different time ame peron a walking peed i not ame among different people and not contant during walking. Fortunately, Wii i a window-baed approach, and it can calculate periodicity dynamically when people walk at definition a certain time window. A a reult, Wii ha ability to adapt to different walking peed a peron. Third, we can ue thi cale to extract correponding cpl that i relative to walking. When collecting CSI data, we alo ue a digital camera to take video record walking. Then, we compare waveform cpl thi cale ( cpl ) with video record and find that waveform between two zero croing point can repreent one tep a hown in 4. However, ome noie in waveform exit; that i, time interval between two adjacent zero croing point i too hort or too long. Thu, we et lower bound t lb a Wavelet.5 and variance. upper bound t ub a. intuitively a time interval mot tep are between e two bound. Finally, cpl Finally, cpl can be can divided be divided into tep into tep egment egment according according to to location location e e point point with time with interval time between interval between t lb and t ub t. and t lb ub Wavelet Wavelet coefficient coefficient waveform waveform tep. tep Feature Extraction 4.4. Feature Extraction During our experiment, we have extracted many feature uch a maximum, minimum, mean During and our ome experiment, or tatitic we feature have extracted waveform, many feature and find uch that a both maximum, time and frequency minimum, meandomain ome feature orcan tatitic be ued feature to repreent waveform, characteritic and find that both human timewalking. and frequency In addition, domain feature per-tep can be feature ued to and repreent walking feature characteritic can be both human extracted walking. identity In addition, identification. per-tep However, feature and e walking feature feature hould can be be both calculated extracted in in a certain identitywindow. identification. Conequently, However, we e ue feature two type hould be calculated window, in tep a certain window window. and walking Conequently, window. A we tep ue window two type i time window, length tep one window tep and and a walking walking window. Acontain tep window everal itep window. time length The candidate one tepfeature and a calculated walking window thi paper contain are everal lited tep in Table window. with The Equation candidate (6) (6). feature In calculated equation, inn thi paper number are lited cpl in Table a tep, m with i Equation number (6) (6). tep In in a walking equation, window, n i cpl number i ith cpl cpl in in atep, tep, and m cpl i i number cpl tep jth i j in a walking window, cpl tep. In Equation (6), we i i ith cpl in tep, and cpl j i cpl jth tep. In Equation (6), divide cpl value in a tep into egment and p i ratio k we divide cpl value in a tep into egment and p k i ratio number cpl kth egment to number total number cpl kth cpl egment in walking to total window. number However, cpl in too many walking feature window. willhowever, lead to a too high computational many feature complexity will lead andto even a high overfitting computational that identification complexity and accuracy even decreae. overfitting Tothat elect mot identification effective feature, accuracy we decreae. calculate To elect normalized mot information effective feature, gain each we calculate feature to quantify normalized ir information gain each feature to quantify ir impact on claification a hown in 5, and impact on claification a hown in 5, and elect Top 5 feature [34]. A higher information elect Top 5 feature [34]. A higher information gain value mean higher capability gain value mean a higher capability repreenting characteritic. A a reult, we chooe repreenting characteritic. A a reult, we chooe maximum (max), minimum (min) and tandard deviation (td) cpl a time-domain feature in tep window, average tep time

8 Senor 7, 7, Senor 7, 7, x 8 8 maximum (at) a (max), time-domain minimum feature (min) in and walking tandard window, deviation and (td) entropy cpl a a frequency-domain time-domain feature in tep in window, tep window. average tep time (at) a time-domain feature in walking window, and entropy a frequency-domain feature in tep window. Table. Candidate feature ID Feature Name Table. Candidate feature. Decription Maximum (max) max = max( cpl ) (6) ID Minimum Feature Name (min) min = Decription min( cpl ) (7) Maximum (max) max = max(cpl ) (6) 3 Mean mean = Minimum (min) min = å cpl i min(cpl ) (7) n i = 4 Median 3 Mean mean = n n median = median( cpl cpl ) (9) i (8) n i= 4 5 Standard Median deviation (td) td = median å ( = cpl median(cpl -mean) )() i (9) n i = n n 5 Standard deviation (td) td = n 6 Skewne (k) (cpli mean) k = ( ) () å cpl - mean () i=i n i = n 6 Skewne (k) k = n n (cpl i mean) 4 å( cpl - mean) () i 7 Kurtoi (ku) i= i= ku = n () (cpli 7 Kurtoi (ku) mean)4 4 ( n-) td i= ku = m () (n )td 4 8 Average tep time (at) at = å 8 Average tep time (at) at = m mlen( cpl ) (3) j m j len(cpl = j) (3) m j= 9 Variance tep time (vt) vt = å 9 Variance tep time (vt) vt = m m(len( cpl ) -at) (len(cpl (4) j m j) at) j = (4) j= Energy energy = n Energy energy = å ( cpl ) i (cpli (5) ) (5) i= i= =-å log (6) k k p k log p k (6) k= k= Entropy entropy p Entropy entropy = p n.6.4 Normalized Information Gain Feature Normalized Normalized information information gain gain candidate candidate feature. feature. In In experiment, we we et et walking window length from to to,, while windowtep tep i i walking walking tep, tep, and and evaluation reult will be be illutrated in Section Training and and Claification A A a peron a peron walking path path ha ha aa great impact on feature wireleignal ignal becaue becaue multipath multipath effect, effect, uually uually identity identification ytem ytemi i placed placed near near entrance entrance room room or or a corridor that people in area walk in a relative fixed path. Thu, cenario training and claification are ame, which enure effectivene training ample.

9 Senor 7, 7, After extracting per-tep and walking feature human walking, we integrate feature everal tep to build people gait prile. Wii firt utilize Gauian Mixture Model (GMM) to ditinguih between aunticated people and tranger and n ue multi-cla SVM with Radial Bai Function (RBF) kernel a claifier to identify people baed on extracted feature. The input GMM are vector elected feature value tranger and aunticated people, and output GMM are probabilitie wher tep belong to a tranger or an aunticated peron. The input SVM are vector elected feature value aunticated people, and output SVM are probabilitie that each tep owner. It i difficult for a claifier to recognize a ubject outide training et. A a reult, we collect everal ample tranger and contruct a tranger prile. The ample are contructed by five feature with highet information gain. The aunticated people can be een a a Gauian model while tranger can be een a anor Gauian model. Conequently, we ue GMM to recognize tranger to aunticate people in monitoring area. The tranger ued in training data are only ome ample aumed tranger. However, when a genuine tranger come, hi imilarity i higher to ample tranger than aunticated people. A a reult, a genuine tranger can alo be recognized. After aunticated people are recognized, identity will be identified. In identification phae, Wii extract ame feature a training phae and ue SVM to identify people. We ue LIBSVM toolbox propoed by Chih-Jen Lin [35] which i widely ued in machine learning. In end claification, we have a pot-proceing procedure, which can furr improve identification accuracy. In pot-proceing, we aume that ucceive tep in a walking window belong to ame peron. It i unlikely to happen in real cenario that Peron A uddenly diappear and Peron B how up to walk for a ingle tep or two tep intead Peron A, and Peron A appear again to continue walking. For example, if identification reult equence in a walking window i AABAA, we can replace B in middle with A in identification reult in thi ituation. The only cot pot-proceing i delay in preenting identification reult, but recognition can be more accurate. 5. Evaluation 5.. Experiment Setup To evaluate performance Wii, we conduct real experiment uing holdout cro validation in a typical meeting room with ize 5 m 4 m a hown in 6. A part data i randomly elected a training data, while or part i ued a tet data. Thi procedure i repeated five time in each evaluation. Each reult i mean value five validation. The meeting room i occupied with a meeting table and chair. We ue a TP-Link 8.n wirele router with a ingle antenna a tranmitter and a Lenovo laptop equipped with a three-antenna Intel WiFi Link 53 (iwl 53) NIC running Ubuntu.4 OS a receiver. Specifically, antenna laptop are modified uing three 6 dbi gain antenna, and firmware NIC i modified to extract CSI from data packet uing CSI tool. The tranmitter and receiver are placed about.75 m above floor and 3 m away from each or. During data collection period, laptop i configured to continuouly end ICMP packet to wirele router and it will receive correponding repone packet. To collect CSI data to obtain precie information people walking activity, ampling rate hould be a high a poible. However, proceing ability wirele router i limited, we adjut ampling rate to be Hz in conequence. The NIC record a CSI ample with CFR 3 3 ubcarrier from each repond packet. We recruit eight healthy volunteer in our experiment and baic information volunteer i hown in Table. The volunteer are aked to walk naturally on path that croe LOS tranceiver without contraint walking peed or tyle. The data collection take five day and

10 Senor 7, 7, 5 7 Senor 7, 7, x 8 a volunteer collection walk take five without day anyone and a volunteer ele in walk meeting without room anyone to reduce ele in noie. meeting After room tepto extraction, reduce we getnoie. aboutafter 5 tep extraction, for each volunteer. we get about 5 tep for each volunteer. Tx Walking Path Rx 6. Experimental cenario. Table Table.. Baic Baic information volunteer volunteer. Volunteer Volunteer Gender Gender Height Height (cm) (cm) Weight Weight (kg) (kg) Age Age male male male male male male male male female male male female female Performance Evaluation Stranger Recognition Stranger recognition i i firt tep human identificationin in ecurity ytem, o o it i ita icrucial a crucial procedure Wii. Wii. Before identifying peron identity, Wii hato to recognize wher peron entering monitoring area i i an aunticated peron or a tranger. After only only five five walking tep, tep, Wii Wii can can determine wher peron i i a tranger. Whenan an aunticatedperon peron i i recognized, identity identity identification identification procedure procedure i activated. i activated. The accuracy The accuracy tranger tranger recognition recognition under under different number different tranger number intranger training et training i hown et i inhown in 7. The tranger 7. The tranger are randomly are randomly elected from elected volunteer from volunteer and modeled and uing modeled uing ame feature ame feature a aunticated a aunticated people. people. The election The election i performed everal time and final reult i average accuracy different i performed everal time and final reult i average accuracy different combination. combination. A can be een, overall trend accuracy decreae a number tranger A can be een, overall trend accuracy decreae a number tranger in training in training et become larger. Thi i becaue, a number tranger in training et et become larger. Thi i becaue, a number tranger in training et increae, feature increae, feature tranger become more and more generally repreentative that feature tranger become more and more generally repreentative that feature aunticated people aunticated people may be ubmerged in thoe tranger. Conequently, number may tranger be ubmerged in training in thoe et hould tranger. not be Conequently, too large. number tranger in training et hould not be too large.

11 Senor Senor 7, 7, 5 7, x 87 Accuracy (%) Performance tranger recognition Evaluation Evaluation Step Step Segmentation Step Step egmentation egmentation i a critical i a critical module module gait analyi gait in analyi ytem. in To ytem. validate To it effectivene, validate it effectivene, we compare Wii with WiWho fairly uing ame feature and claifier a that we compare Wii with WiWho fairly uing ame feature and claifier a that WiWho a well a WiWho a well a experimental etup. Concretely, feature include feature ID,, 5, 8, experimental etup. Concretely, feature include feature ID,, 5, 8, and in Table and in Table and claifier i deciion tree. There are eight volunteer in training et. and claifier i deciion tree. There are eight volunteer in training et. The ize training The ize training et varie from to 5 tep. The ize walking window i five tep. The et varie from to 5 tep. The ize walking window i five tep. The identification accuracy identification accuracy two approache i hown in 8. It It can be een that identification two approache accuracy i hown both in approache 8. It rie can a be een ize that training identification et grow. accuracy The accuracy both approache Wii grow rie fater a and ize keep training higher than et grow. that WiWho The accuracy a ize Wii grow training fater et become and keep larger. higher It It than indicate that WiWho that a tep egmentation ize training module et become Wii can larger. extract It indicate more effective that tep tep information egmentation and module ha a better Wii can performance. extract more effective tep information and ha a better performance Step egmentation Identity Identity Identification Identification with with Different Different Number Number Feature The The number number feature feature ha ha impact impact on on both both computation computation complexity complexity and and identification identification accuracy. We elect firt n feature that have highet information gain, and eight volunteer accuracy. We elect firt n feature that have highet information gain, and eight volunteer are are included in thi evaluation. The evaluation reult i depicted in 9. The identification included in thi evaluation. The evaluation reult i depicted in 9. The identification accuracy accuracy firt rie with number feature. However, accuracy i not changing firt rie with number feature. However, accuracy i not changing monotonically with monotonically with number feature. It It tart to decreae when number feature number feature. It tart to decreae when number feature become larger than 6 becaue become larger than 6 becaue an exce number feature lead to overfitting to ytem. A a an exce number feature lead to overfitting to ytem. A a reult, five feature with highet information gain give a good trade-f between computation complexity and accuracy.

12 Senor 7, 7, x 8 reult, five feature with highet information gain give a good trade-f between Senor 7, 7, 5 7 computation complexity and accuracy. Accuracy (%) Impact Impact different different number number feature. feature Identity Identity Identification with with Different Size Training Set In thi In thi ection, ection, we we preent performance Wii Wii when ize ize training et et varie. The ize training training et varie et from varie from to 5 tep to 5 fortep one ubject, for one and ubject, eightand ubject eight are ubject included are included training et. Thetraining et. The ize walking window i five tep. In addition, to tudy advancement Senor ize 7, 7, walking x window i five tep. In addition, to tudy advancement Wii, 3 we 8 alo compare Wii, we alo performance compare with performance WiWho and with FreeSene. WiWho and TheFreeSene. deign cenario The deign WiWho cenario and FreeSene WiWho are almot and FreeSene ame are a almot Wii that all ame m a work Wii that in home all or m mall work fice in environment. home or mall A a fice reult, environment. A a reult, we implement WiWho and FreeSene uing ame experimental etup we implement WiWho and FreeSene uing ame experimental etup a Wii. According to, a Wii. According to, accuracy Wii keep increaing from 75.8% to 9.4% a ize accuracy Wii keep increaing from 75.8% to 9.4% a ize training et grow. A indicated training et grow. A indicated in figure, accuracy Wii increae fater when ize in figure, accuracy Wii increae fater when ize training et grow from to 3, training et grow from to 3, while low down a it change to 4 and 5. Thi mean while low down a it change to 4 and 5. Thi mean performance Wii become table when performance Wii become table when ize i 4 or larger. The paired t-tet i ued to analyze ize difference i 4 or larger. The reult paired between t-tet Wii i ued and to analyze or two method. difference The tet reult reult are between hown in Wii andtable or 3, where two method. P i poibility The tet reult t-tet are between hownwii in Table and WiWho, 3, wherewhile P ip i poibility poibility t-tet between t-tet Wii between and WiWho, Wii and FreeSene. while P ia can poibility be een, all reject t-tet between null hypoi, Wii and FreeSene. which mean A can that be een, allreult reject are null independent hypoi, from which ample meanelection. that reult In addition, are independent we ue from Kappa ample index election. to In addition, evaluate we ue claification Kappa agreement index to[36]. evaluate The Kappa claification index agreement three method [36]. The under Kappa different index ize three training method et under i hown different in ize. training Wii ha a et higher i hown Kappa in index. compared Wii hato a higher or Kappa two index method, compared which to mean orthat twowii method, ha a higher which mean agreement. that Wii In one haword, a higher Wii agreement. perform better In onethan word, Wii WiWho performand better FreeSene. than WiWho It i becaue and FreeSene. feature It i becaue elected oretically feature elected contain oretically more information, contain more and information, y are more anduitable y are to more claifier uitable. Impact and to need claifier ize a maller training and training need et. et. a maller training et. Table 3. P value paired t-tet different ize training et..95 Size Training.9 Set (Step) P P Size Training Set (tep) Wii WiWho FreeSene.. Kappa Kappa index index under different ize training traininget. et Identity Identification with Different Group Size We n evaluate Wii with different group ize. The ize group increae from two to eight peron, and ize training et i 4. We fix ize training et to 4 tep becaue performance rie to a table level when ize training et i larger than 3 according to. For cae that ize group i 7, we randomly elect 7 peron from eight volunteer, and Wii i trained before claification.

13 Senor 7, 7, Table 3. P value paired t-tet different ize training et. Size Training Set (Step) P Senor 7, 7, x P Impact Impact ize ize training training et. et Identity Identification with Different Group Size.95 We n evaluate Wii with.9 different group ize. The ize group increae from two to eight peron, and ize training et i 4. We fix ize training et to 4 tep becaue.85 performance rie to a table level when ize training et i larger than 3 according to..8 For cae that ize group i 7, we randomly elect 7 peron from eight volunteer,.75 and Wii i trained before claification. A hown in,.7accuracy Wii decreae from 98.7% to 9.9% a group ize get larger. It i becaue re are imilar feature among different people, and when group ize.65 get larger, imilar feature add more confuion into ytem, which make it more difficult to Wii.6 WiWho identify a peron. The ame trend alo happen in WiWho and FreeSene, but accuracy decreae.55 quickly when group ize get larger than 4. Conequently, comparion reult indicate that Wii Senor 7, 7, x Size Training Set (tep) 4 8 perform more tably when group ize get larger.. Kappa index under different ize training et Identity Identification with Different Group Size We n evaluate Wii with different group ize. The ize group increae from two to eight peron, and ize training et i 4. We fix ize training et to 4 tep becaue performance rie to a table level when ize training et i larger than 3 according to. For cae that ize group i 7, we randomly elect 7 peron from eight volunteer, and Wii i trained before claification. A hown in, accuracy Wii decreae from 98.7% to 9.9% a group ize get larger. It i becaue re are imilar feature among different people, and when group ize get larger, imilar feature add more confuion into ytem, which make it more difficult to identify a peron. The ame trend alo happen in WiWho and FreeSene, but accuracy decreae quickly when group ize get larger than 4. Conequently, comparion reult indicate that Wii perform more tably when group ize get larger... Performance with different group ize Identification Accuracy Different People The identification reult eight volunteer in above experiment i hown in confuion matrix in 3. It how detail identification different people. It i obviou that different people have different identification accuracy and it varie from 83% to 95%. According to Table, reult that Volunteer 6 and 8 have highet accuracy a e two volunteer are only female volunteer in tet group, which indicate that gait pattern female ha le imilarity with male. We can alo find that, if two people have imilar height and

14 Senor 7, 7, Performance with different group ize Identification Accuracy Different People Identification Accuracy Different People The identification reult eight volunteer in above experiment i hown in confuion The identification reult eight volunteer in above experiment i hown in matrix confuion in matrix 3. in It how 3. It detail how detail identification identification different people. different It i people. obviou It i that different obviou people that different have different people identification have different accuracy identification andaccuracy it varieand fromit 83% varie trom 95%. 83% According to 95%. to Table According, reult to Table that Volunteer, reult 6 and that 8 have Volunteer highet 6 and accuracy 8 have a e highet twoaccuracy volunteer a e are two only female volunteer volunteer are in only tet female group, volunteer which indicate tet that group, gait which pattern indicate female that ha gait le pattern imilarity withfemale male. ha Wele canimilarity alo find with that, male. if twowe people can alo havefind imilar that, height if two and people weight, have imilar y areheight moreand likely to have weight, difficultie y are being more correctly likely to identified. have difficultie Epecially, being identification correctly identified. accuracy Epecially, Volunteer 7 i identification highet among accuracy male Volunteer volunteer, 7 i a highet he ha aamong higher height male than volunteer, or a he male ha volunteer. a higher In or height word, than height or and male weight volunteer. have ain ignificant or word, impact height onand weight tranmiion have a ignificant wireleimpact ignal. on tranmiion wirele ignal. Claified a Actual peron The confuion matrix human identification with eight people. 3. The confuion matrix human identification with eight people Impact Walking Window Size Impact Walking Window Size There i alo anor important factor in evaluation Wii that can affect identification There i alo anor important factor in evaluation Wii that can affect identification accuracy. A FreeSene i not a window-baed approach, it i excluded in evaluation different accuracy. A FreeSene i not a window-baed approach, it i excluded in evaluation different walking window ize. We furr compare performance Wii with WiWho under different walking window ize. We furr compare performance Wii with WiWho under different walking window ize when ize training et i 4 and group ize i 8. walking window ize when ize training et i 4 and group ize i 8. A A can can be be een een in in 4, 4, we we change change ize ize walking walking window window from from to to.. The The mallet mallet window window ize ize i i i becaue i becaue it i it i mallet mallet ize larger ize larger than athan ingle a tep. ingle Iftep. window If window ize decreae ize to, walking feature decline to per-tep feature, which make no ene. We think it i till practical for ytem that identification reult come out after uer walk for tep if we can get a higher accuracy. From evaluation reult we can ee that, when ize walking window i maller than five tep, identification accuracy Wii keep growing a window ize get larger, which indicate that our walking feature play a crucial role in improving performance identity identification. However, accuracy become table when window ize get larger than five tep, which mean that it i appropriate to et ize walking window to 5. The accuracy 9.9% can atify common mart home application. Obviouly, Wii can be better adopted in a larger group ize.

15 tep if we can get a higher accuracy. From evaluation reult we can ee that, when ize walking window i maller than five tep, identification accuracy Wii keep growing a window ize get larger, which indicate that our walking feature play a crucial role in improving performance identity identification. However, accuracy become table when window ize get larger than five tep, which mean that it i appropriate to et ize Senor walking 7, 7, window 5 to 5. The accuracy 9.9% can atify common mart home application. 5 7 Obviouly, Wii can be better adopted in a larger group ize. 95 Wii WiWho 9 Accuracy (%) Walking Window Size Impact walking window ize. 6. Dicuion 6. Dicuion We did everal evaluation in thi work and howed feaibility identity identification We did everal evaluation in thi work and howed feaibility identity identification uing uing WiFi ignal. However, re till exit ome limitation in Wii. In thi ection, we will dicu WiFi ignal. However, re till exit ome limitation in Wii. In thi ection, we will dicu limitation and potential Wii, which give direction our future work. limitation and potential Wii, which give direction our future work. Although Wii can achieve a relatively high identification accuracy, it can alo be affected by many Although factor. Wii can achieve a relatively high identification accuracy, it can alo be affected by many factor. Firt, we have collected walking data in everal location and different path. At firt, we planned Firt, we to have identify collected people walking even if he data changed in everal hi path. location However, and different extracted path. feature At firt, change we planned a lot to identify when people peron even walk if he in changed anor path. hi path. We cannot However, uccefully extracted identify feature a peron change when a lottraining when peron data walk and tet in anor data are path. collected We cannot at different uccefully path. Specifically, identify awe peron alo when find that training identification data and tet accuracy data are collected i higher when at different peron path. walk Specifically, acro weline--ight alo find that identification tranceiver than accuracy or i higher path. when A a reult, peron we walk contraint acro walking line--ight path our experiment. tranceiver than or path. A a reult, we contraint In addition, walking people path walking ourgait experiment. indeed ha a great impact on identification reult. Thu, during In addition, data collection, people walking volunteer gait indeed are aked ha ato great keep impact ir walking on identification gait and walk reult. naturally. Thu, during WLAN-baed data collection, human identification volunteerha areome akedimilaritie to keep ir with radar walking technique. gait andthu, walkdifferent naturally. WLAN-baed poe may human caue different identification fingerprint ha ome for imilaritie ame peron, withwhich radarinfluence technique. Thu, accuracy. different poe may caue Furrmore, different fingerprint WiFi ignal for can be ame eaily peron, affected which by environmental influence change. accuracy. Thu, Wii can only be ued when re i only one peron walking in monitoring area without anyone ele. Furrmore, WiFi ignal can be eaily affected by environmental change. Thu, Wii can only be Depite e limitation, wirele ignal-baed identity identification technique ha much ued when re i only one peron walking in monitoring area without anyone ele. potential. Beide intruion detection, it can alo be ued a a critical function in behavior analyi Depite e limitation, wirele ignal-baed identity identification technique ha much ytem a well a to provide peronalized ervice in mart pace. potential. Beide intruion detection, it can alo be ued a a critical function in behavior analyi In our future work, we plan to explore new feature that can repreent people gait pattern ytem more a accurately well a toin provide order to peronalized make identity ervice identification in mart pace. ytem have ability to be adaptively ued In our even future when work, uer we plan walk toin explore different new path. feature that can repreent people gait pattern more accurately in order to make identity identification ytem have ability to be adaptively ued even when uer walk in different path. 7. Concluion In thi paper, we propoe an effective identity identification approach Wii only baed on WiFi ignal which bring much convenience in mart home application. It utilize exiting WLAN infratructure and i baed on fine-grained CSI from phyical layer wirele network. Wii divide time erie walking into egment by tep and extract both time and frequency domain feature from per-tep and walking egment perpective. The extracted feature can be properly ued in repreenting human gait pattern. It can effectively recognize tranger. To evaluate performance Wii, we implement a et experiment from everal perpective. The reult how that Wii achieve an average identification accuracy 98.7% when re are two people, and 9.9% when re

16 Senor 7, 7, are eight people, which i effective and can atify typical home ecurity. The limitation and potential WiFi ignal-baed identity identification ytem are alo dicued in thi work. Acknowledgment: Thi reearch i upported by National Natural Science Foundation China (Grant No. 636, and 6776). Author Contribution: Jiguang Lv and Wu Yang conceived and deigned tudy; Jiguang Lv and Dapeng Man conceived and deigned experiment; Jiguang Lv performed experiment; Dapeng Man analyzed data; Jiguang Lv wrote paper; and Wu Yang and Dapeng Man reviewed and edited manucript. All author read and approved manucript. Conflict Interet: The author declare no conflict interet. Reference. Karu, K.; Jain, A.K. Fingerprint Claification. Pattern Recognit. 996, 9, [CroRef]. Chellappa, R.; Wilon, C.L.; Sirohey, S. Human and Machine Recognition Face: A Survey. Proc. IEEE 995, 83, [CroRef] 3. Park, H.-A.; Park, K.R. Iri Recognition Baed on Score Level Fuion by Uing SVM. Pattern Recognit. Lett. 7, 8, 9 8. [CroRef] 4. Niu, J.; Wang, B.; Cheng, L.; Rodrigue, J.J.P.C. WicLoc: An Indoor Localization Sytem baed on WiFi Fingerprint and Crowdourcing. In Proceeding IEEE International Conference on Communication (ICC), London, UK, 8 June 5; pp Youef, M.; Mah, M.; Agrawala, A. Challenge: Device-free paive localization for wirele environment. In Proceeding 3th Annual ACM International Conference on Mobile Computing and Networking, Montreal, QC, Canada, 9 4 September 7; pp Zeng, Y.; Pathak, P.H.; Mohapatra, P. WiWho: WiFi-baed Peron Identification in Smart Space. In Proceeding 5th International Conference on Information Proceing in Senor Network, Vienna, Autria, 4 April 6; pp.. 7. Xin, T.; Guo, B.; Wang, Z.; Li, M.; Yu, Z.; Zhou, X. FreeSene: Indoor Human Identification with Wi-Fi Signal. In Proceeding IEEE Global Communication Conference (GLOBECOM), Wahington, DC, USA, 4 8 December 6; pp Zhang, J.; Wei, B.; Hu, W.; Kanhere, S.S. WiFi-ID: Human Identification Uing WiFi Signal. In Proceeding International Conference on Ditributed Computing in Senor Sytem (DCOSS), Wahington, DC, USA, 6 8 May 6; pp Liam, L.W.; Chekima, A.; Fan, L.C.; Dargham, J.A. Iri Recognition Uing Self-Organizing Neural Network. In Proceeding Student Conference on Reearch and Development, Shah Alam, Malayia, 7 July ; pp Bhattacharyya, D.; Da, P.; Bandyopadhyay, S.K.; Kim, T. Iri Texture Analyi and Feature Extraction for Biometric Pattern Recognition. Int. J. Databae Theory Appl. 8,, Wang, Y.; Hu, J.; Han, F. Enhanced Gradient-Baed Algorithm for Etimation Fingerprint Orientation Field. Appl. Math. Comput. 7, 85, [CroRef]. Yang, J.; Ge, Y.; Xiong, H.; Chen, Y.; Liu, H. Performing Joint Learning for Paive Intruion Detection in Pervaive Wirele Environment. In Proceeding Proceeding IEEE INFOCOM, San Diego, CA, USA, 4 9 March ; pp Koba, A.E.; Saeed, A.; Youef, M. RASID: A Robut WLAN Device-Free Paive Motion Detection Sytem. In Proceeding IEEE International Conference on Pervaive Computing and Communication, Lugano, Switzerland, 9 3 March ; pp Zhang, D.; Liu, Y.; Guo, X.; Ni, L.M. RASS: A Real-Time, Accurate, and Scalable Sytem for Tracking Tranceiver-Free Object. IEEE Tran. Parallel Ditrib. Syt. 3, 4, [CroRef] 5. Depatla, S.; Muralidharan, A.; Moti, Y. Occupancy Etimation Uing Only WiFi Power Meaurement. IEEE J. Sel. Area Commun. 5, 33, [CroRef] 6. Halperin, D.; Hu, W.; Sheth, A.; Werall, D. Tool Releae: Garing 8. n Trace with Channel State Information. ACM SIGCOMM Comput. Commun. Rev., 4, 53. [CroRef] 7. Liu, W.; Gao, X.; Wang, L.; Wang, D. Bfp: Behavior-Free Paive Motion Detection Uing PHY Information. Wirel. Per. Commun. 5, 83, [CroRef]

17 Senor 7, 7, Qian, K.; Wu, C.; Yang, Z.; Liu, Y.; Zhou, Z. PADS: Paive Detection Moving Target with Dynamic Speed Uing PHY Layer Information. In Proceeding th IEEE International Conference on Parallel and Ditributed Sytem (ICPADS), Hinchu, Taiwan, 6 9 December 4; pp Lv, J.; Yang, W.; Gong, L.; Man, D.; Du, X. Robut WLAN-Baed Indoor Fine-Grained Intruion Detection. In Proceeding IEEE Global Communication Conference (GLOBECOM), Wahington, DC, USA, 4 8 December 6; pp. 6.. Domenico, S.D.; Sancti, M.D.; Cianca, E.; Bianchi, G. A Trained-once Crowd Counting Method Uing Differential WiFi Channel State Information. In Proceeding 3rd International on Workhop on Phyical Analytic, Singapore, 6 June 6; pp Yuan, Y.; Zhao, J.; Qiu, C.; Xi, W. Etimating Crowd Denity in an RF-Baed Dynamic Environment. IEEE Sen. J. 3, 3, [CroRef]. Domenico, S.D.; Pecoraro, G.; Cianca, E.; Sancti, M.D. Trained-Once Device-Free Crowd Counting and Occupancy Etimation Uing WiFi: A Doppler Spectrum Baed Approach. In Proceeding th IEEE International Conference on Wirele and Mobile Computing, Networking and Communication (WiMob), New York, NY, USA, 7 9 October 6; pp Xi, W.; Zhao, J.; Li, X.Y.; Zhao, K.; Tang, S.; Liu, X.; Jiang, Z. Electronic Frog Eye: Counting Crowd Uing WiFi. In Proceeding IEEE Conference on Computer Communication, Toronto, ON, Canada, 7 April May 4; pp Wang, Y.; Liu, J.; Chen, Y.; Gruteer, M.; Yang, J.; Liu, H. E-eye: Device-Free Location-Oriented Activity Identification Uing Fine-Grained WiFi Signature. In Proceeding th Annual International Conference on Mobile Computing and Networking, Maui, HI, USA, 7 September 4; pp Wang, W.; Liu, A.X.; Shahzad, M.; Ling, K.; Lu, S. Undertanding and Modeling WiFi Signal Baed Human Activity Recognition. In Proceeding t Annual International Conference on Mobile Computing and Networking, Pari, France, 7 September 5; pp Sun, L.; Sen, S.; Koutonikola, D.; Kim, K.-H. WiDraw: Enabling Hand-free Drawing in Air on Commodity WiFi Device. In Proceeding t Annual International Conference on Mobile Computing and Networking, Pari, France, 7 September 5; pp Zheng, X.; Wang, J.; Shangguan, L.; Zhou, Z.; Liu, Y. Smokey: Ubiquitou Smoking Detection with Commercial WiFi Infratructure. In Proceeding 35th Annual IEEE International Conference on Computer Communication, San Francico, CA, USA, 4 April 6; pp Tan, S.; Yang, J. WiFinger: Leveraging commodity WiFi for fine-grained finger geture recognition. In Proceeding 7th ACM International Sympoium on Mobile Ad Hoc Networking and Computing, Paderborn, Germany, 5 8 July 6; pp.. 9. Unar, J.A.; Seng, W.C.; Abbai, A. A review Biometric Technology Along with Trend and Propect. Pattern Recognit. 4, 47, [CroRef] 3. Wang, W.; Liu, A.X.; Shahzad, M. Gait Recognition Uing WiFi Signal. In Proceeding ACM International Joint Conference on Pervaive and Ubiquitou Computing, Heidelberg, Germany, 6 September 6; pp Yang, Z.; Zhou, Z.; Liu, Y. From RSSI to CSI: Indoor Localization via Channel Repone. ACM Comput. Surv. 3, 46, 3. [CroRef] 3. Daubechie, I.; Bate, B.J. Ten Lecture on Wavelet. J. Acout. Soc. Am. 993, 93, 67. [CroRef] 33. Carey, C.C.; Hanon, P.C.; Lathrop, R.C.; St. Amand, A.L. Uing Wavelet Analye to Examine Variability in Phytoplankton Seaonal Succeion and Annual Periodicity. J. Plankton Re. 6, 38, 7 4. [CroRef] 34. Witten, I.H.; Frank, E.; Hall, M.A. Data Mining: Practical Machine Learning Tool and Technique; Morgan Kaufmann Publiher Inc.: San Francico, CA, USA, ; p Chang, C.-C.; Lin, C.-J. LIBSVM: A Library for Support Vector Machine. ACM Tran. Intell. Syt. Technol.,, 7. [CroRef] 36. Cohen, J. A Coefficient Agreement for Nominal Scale. Educ. Pychol. Mea. 96,, [CroRef] 7 by author. Licenee MDPI, Bael, Switzerland. Thi article i an open acce article ditributed under term and condition Creative Common Attribution (CC BY) licene (