Arm Swing as a Weak Biometric for Unobtrusive User Authentication

Transcription

1 International Conference on Intelligent Information Hiding and Multimedia Signal Processing Arm Swing as a Weak Biometric for Unobtrusive User Authentication Davrondzhon Gafurov and Einar Snekkenes Norwegian Information Security Lab, Gjøvik University College, P.O. Box 191, 2802 Gjøvik, Norway, davrondzhon.gafurov@hig.no, einars@hig.no Abstract Verifying the identity of a user before granting access to objects or services is an important step in nearly all applications or environments. Some applications (e.g. pervasive environment) may impose additional requirements for user authentication mechanism, such as to be continuous and unobtrusive. The former refers to constant or periodic re-assurance of the identity, while the latter means that the authentication mechanism should be unobtrusive and implicit (without attracting user s attention). Traditional user authentication mechanisms (e.g. passwords or fingerprints) cannot be or is difficult to adapt to fulfill such requirements. This paper studies an unobtrusive mechanism of user authentication based on a new biometric modality. Arm swing, which occurs during gait, is proposed as a weak biometric for user authentication. We collected arm swing of the person by using a motion recording sensor, which records acceleration of the arm swing in three orthogonal directions. Using frequency domain analysis of the arm swing accelerations, we obtained an Equal Error Rate (EER) of 10% based on a preliminary data set including 120 arm swing samples from 30 persons. 1 Introduction Verifying the identity of a user before granting access to objects or services is an important step in nearly all applications or environments. Some applications (e.g. pervasive environment) may impose additional requirements for user authentication mechanism, such as to be continuous and unobtrusive. The continuous aspect of authentication refers to the constant or periodic re-verification of the identity. The second aspect (i.e. unobtrusiveness) refers to the fact that the authentication procedure should be convenient, implicit and without requiring user s explicit cooperation. For example, in pervasive environment the electronic devices are carried by the user almost all the time. However, the devices are not always under the attention of their owners, e.g. some people tend to forget, leave unattended or even lose the devices. Current authentication mechanisms in many personal electronic devices (e.g. mobile phones) are static. In other words, a user authenticates once (e.g. by entering a PIN code) and authentication lasts until the device is turned off. Consequently, the single-time (i.e. static) authentication is not sufficient, especially when the devices are used in high security applications. For instance, nowadays mobile phones can be used in application like m-banking or m-commerce [1]. On the other hand, in a pervasive system, the seamless interaction between the user and the device is a very important criteria. The conventional user authentication mechanisms (e.g. password-based or fingerprintbased), cannot be or is difficult to accommodate in such applications to meet continuous and unobtrusiveness requirements. Indeed, the process of frequently entering password or providing fingerprint on a mobile phone is explicit, requires user cooperation and can be very inconvenient and annoying. Therefore, better mechanisms for unobtrusive and continuous user authentication is required. Recently, identifying people by the way they walk became one of the attractive topics in biometric research [2]. Person s manner of walking is called gait, and the approach is called biometric gait recognition. One of the primary advantages of biometric gait recognition is in providing mechanisms for unobtrusive person verification and identification. From a technological perspective, biometric gait recognition is categorized into three groups [3]: Machine Vision (MV) based, Floor Sensor (FS) based and Wearable Sensor (WS) based. In the MV-based approach, gait is captured using a video-camera and then image/video processing techniques are applied to extract gait features for recognition [4, 5, 6]. In the FS-based approach, gait of the person is captured using sensors (e.g. force plates) installed in the /08 $ IEEE DOI /IIH-MSP

2 frequency domain analysis of the arm swing accelerations, we obtained an Equal Error Rate (EER) of about 10%, and identification probability of 71.7% at rank 1. The remainder of the paper is structured as follows. Section 2 outlines the authentication technology. Section 3 and 4 contains experiment description and results of analysis, respectively. Section 5 contains discussion and section 6 concludes the paper. 2 User authentication technology 2.1 Motion Recording sensor Figure 1. Accelerometer sensor attached to the hip [13]. For collecting arm movement, we use a Motion Recording (MR) sensor, see Figure 3. The MR sensor measures acceleration in three orthogonal directions, up-down, forwardbackward and sideways. The sampling frequency of the MR sensor is about 100 samples per second. Besides accelerometers, the other main components of the MR sensor include an internal memory of 64MB for storing acceleration values and a re-chargeable battery. It also has a USB port for transferring collected data to a PC. During the experiment the MR sensor was attached to the lower arm as shown in Figure Verification method Figure 2. Accelerometer sensor attached to the lower leg [14]. floor [7, 8, 9, 10]. In the WS-based category, gait is collected using motion recording sensors attached to various locations on the body of the person [11, 12, 13]. The motion of body locations such as hip (see Figure 1), leg (see Figure 2) and so on have been utilized for person recognition [11, 12, 13]. In this paper, we investigate the feasibility of using the natural arm swing, which occurs during gait, for unobtrusive user authentication. The proposed approach belongs to the WS-based category. We collect arm movement by using a so called Motion Recording (MR) sensor, which is attached to the lower arm of the person. The MR sensor records acceleration of movement in three directions: updown, forward-backwards and sideways. Our data set consists of 120 arm swing samples from 30 persons. Using The first step is the pre-processing, where transformation and interpolation of the acceleration signal are made. Then, the signal is analyzed in frequency domain, where features are extracted. Maximum values in the specified frequency ranges is used as features. The Euclidean distance between two feature vectors is used as a similarity score. A more detailed steps involved in comparing arm movement samples are as follow. 1. Pre-processing: From the output of the MR sensor we get acceleration in three orthogonal directions. These accelerations are transformed and combined in order to obtain more orientation invariant acceleration signal. The time interval between accleration recordings in the signal is not always equal, so we interpolate the signal to get equally sampled signal. An example of the arm swing acceleration is depicted in Figure 5a. 2. Frequency domain analysis: A time varying signal, r(t), can be represented by successively adding the individual frequencies present in the signal as r(t) =a 0 + [b i sin(2πf i t)+c i cos(2πf i t)], (1) where b i and c i are called Fourier coefficients. Based on the Fourier coefficients, the amplitude of the signal at each frequency can be computed as follow, 1081

3 Figure 3. The MR sensor. Figure 4. The MR sensor attached to the arm. A i = b 2 i + c2 i. (2) We analyze the arm swing signals in the frequency domain. Each arm signal is about 10 seconds of arm swing (1024 samples), see Figure 5a. Before transforming the signal into frequency domain its mean is extracted to get zero-mean signal. Then, to reduce the leakage the Hamming window is applied and the Fourier coefficients is computed using FFT (Fast Fourier Transform) algorithm. Next, the amplitude is computed using formula (2). An example of amplitude of the signal is depicted in Figure 5b. 3. Features: We divide the frequency axis of the signal into several ranges. Each frequency range, i, isthe interval ((i 0.5)Hz,(i +0.5)Hz]. High amplitudes are dominant only in the low frequencies; therefore we focus on the first 6 frequency ranges. The maximum amplitudes in each specified frequency ranges are used as features. Then, these features are concatenated to form a feature vector. 4. Feature vectors: Three feature vectors are tested. The first one has two features and it is the maximum amplitudes in the frequency ranges (0.5Hz,1.5Hz] and (1.5Hz,2.5Hz]. The second feature vector has 4 features, which are 4 max amplitudes in the first four frequency ranges, i.e. (0.5Hz,1.5Hz], (1.5Hz,2.5Hz], (2.5Hz,3.5Hz], and (3.5Hz,4.5Hz]. The third feature vector has 6 features, i.e. 6 max amplitudes in the first six frequency ranges (0.5Hz,1.5Hz], (1.5Hz,2.5Hz],..., (5.5Hz,6.5Hz]. Assume V 1,V 2 and V 3 are the three aforementioned feature vectors, respectively. They are related to each other as follow V 1 V 2 V Similarity score: Euclidean distance between two feature vectors are used as a similarity score. Assume V =(v 1,..., v n ) and W =(w 1,..., w n ) are the template and test feature vectors, where v i and w i are maximum amplitudes in the frequency range i of the template and test, respectively. Then the similarity score, S, is calculated as follow, S(V,W)= n (v i w i ) 2,n=2, 4, 6 (3) i=1 The similarity score indicates how similar two arm movement samples are. Ideally, similarity scores obtained from the same person should be smaller then similarity scores obtained from different persons. 3 Experiment Using the MR sensor, we have collected acceleration of the arm swing from 30 subjects, 23 male and 7 female (in age range years old). Subjects were told to walk normally at their natural gait on a level surface for the distance of about 20 meters. The experiment was conducted in 4 sessions. In the first two sessions, the MR sensor was attached to the right arm, and in the last two trials the MR sensor was attached to the left arm. The MR sensor was attached to the lower arm of the subjects as shown in Figure 4. Before each walking trials, subjects were asked to shift the MR sensor 1082

4 Figure 5. An example of arm swing signal: a) Time domain; b) Frequency domain (only low frequency components are depicted). a little bit in order to simulate realistic situations (i.e. the sensor is not exactly on the same position or orientation). In total, there were 120 arm swing samples, 4 samples per person, 2 from the right arm and 2 from the left arm. The recorded accelerations were transferred to the computer for analysis. 4 Results Assuming left and right arm movements are not identical, we divide the set of samples into two non-overlapping sets, S 1 and S 2. The first set, S 1, is the set of all samples from the right arm, while the second set, S 2,isthesetof all samples from the left arm. The cardinalities of each set equals to 60. For each set, we apply the leave-one-out cross comparisons procedure [13]. In this way using each set, 30 genuine and 870 impostor scores are obtained. In total, there will be 60 unique genuine and 1740 unique impostor scores. Based on the sets of genuine and impostor scores, the False Accept Rate (FAR) and False Reject Rate (FRR) are estimated. To evaluate the performance of the method in verification mode, we use a Decision Error Trade-off curve (DET). The DET curve is a plot of FAR versus FRR. The DET curve 1083

5 Figure 6. The performance of the method in terms of DET curves. Figure 7. The performance of the method in terms of CMC curves. shows the performance of a biometric system under different decision thresholds. The corresponding DET curves using three feature sets are depicted in Figure 6. Usually, to indicate the performance of the biometric system by a single value an Equal Error Rate (EER) or a minimal Total Error Rate (TER min )isused.theeer is a point on the curve where FAR=FRR, while the TER min is a point where the sum of FAR and FRR is minimal. The EER and TER min of the method are given in Table 1 (columns EER and TER min, respectively). The best EER and TER min are about 10% and 18.7%, respectively. Using parametric approach as in [15], we can compute the margin of errors (i.e. 95% confidence intervals) for FAR and FRR of the EER, respectively. They are 10 ± 1.4 and 10 ± 7.6 for FAR and FRR, respectively. The margins for FAR are more narrower compared to the margins for FRR, since we have more impostor scores than genuine scores. We also evaluated the performance of the method in identification mode. To evaluate performance in this mode, we use a Cumulative Match Characteristic (CMC) curve [16]. The CMC curve is a plot of the rank vs. the identification probability. It indicates the cumulative probability of an unknown sample being within the top closest matches. Using sets S 1 and S 2 (i.e. right and left samples), two CMC curves were calculated. Then, identification probabilities at each rank are averaged to get one CMC curve. The resulting CMC curves for three feature sets are shown in Figure 7. The averaged identification probabilities at rank 1 of the three feature vectors are given in Table 1 (column P 1 ). Table 1. Performance of the method for 3 feature sets. No. of features EER,% TER min,% P 1,% Two features Four features Six features Discussion Table 2 contains the summary of some WS-based and FS-base works that can be suitable for user recognition in pervasive environment. In this table, the columns Location, S# and Performance, % represent the location of the motion recording sensor(s) on the body or on the floor, the number of subjects used in the experiments and the performance of the methods in term of the EER and/or recognition rate (or identification rate at rank 1). This table does not imply direct comparison of results, but its purpose is merely to give an impression of performances in the WS-based and FSbased person recognition. In a pervasive environment, the personal electronic devices are always with the user and the continuous and unobtrusive (re-) verification of the user is a very important requirement. User authentication using arm swing provides unobtrusive identity verification because the arm swing is a natural motion of the hands/arms that occurs during gait and does not require an extra action from the person. It can be also very well adapted in the continuous authentication con- 1084

6 Table 2. Summary of some works on WS-based and FS-based gait recognition Study Location S# Performance, % EER Recognition rate Ailisto et al. [11] waist Mäntyjärvi et al. [17] waist 36 7, 10, 18, 19 - Gafurov et al. [14] lower leg 21 5, 9 - Vildjiounaite et al. [18] hip and chest (gait+voice) pockets, hand Gafurov et al. [12] trouser pocket , 9.2, 14, , 83.8, 50.5, 24.2 Gafurov et al. [13] hip Orr and Abowd [7] on the floor Suutala and Rning [8] on the floor Middleton et al. [9] on the floor This paper lower arm 30 10, 13.3, , 60, 31.7 Table 3. Performance of some other biometrics in terms of EER. Biometric EER,% Data set Iris [19] subjects Fingerprint [20] FVC2002 DB1 and DB2 [21] Palmprint [22] samples from 392 palms Signature [23] samples and 94 subjects text. Walking (and arm swinging) may happen from several times to many times during normal daily activity. Consequently, it is reasonable to assume that arm swing will be available several or many times per day for re-verification of the user identity. The accuracy of the arm swing biometric is not comparable with the strong biometric modalities, e.g. see performances of some biometrics in Table 3. It should be noted that we do not propose arm swing as a replacement or sole mechanism of authentication but rather as a complementary mechanism that can be used to improve security in personal devices. Users can still use strong biometrics or password explicitly when authenticating for the first time. Then, arm swing biometric can be applied implicitly for re-verification of the identity in continuous authentication scenario. In order to reduce the inconvenience for the genuine users, one can set operating threshold of the system such that FRR is very low or zero and FAR is medium to high levels, e.g. at a ZeroFRR. The ZeroFRR is the minimum FAR, where FRR is zero. In our method the ZeroFRR is about 70% (using 6 features). In general, this suggests that by using the arm swing as an additional level of security, about 30% of attackers, which overcome/spoof first authentication, can be defeated without causing usability inconvenience that would not be possible without this added level. Some part of the work by Vildjiounaite et al. [18] is similar to the approach in this paper, but there are several significant differences between them. Apart differences in recognition methods, data sets and evaluation modes, the other main difference is that Vildjiounaite et al. [18] attached accelerometer to the handle of a suitcase and analyse hand motion with carrying a suticase. In addition, their study shows that the accuracy can be improved when gait is integrated with other types of unobtrusive biometrics [18]. Although the current prototype of the MR sensor looks a bit bulky, it is feasible to shrink the size of MR components and integrate it with the actual electronic device worn in the wrist (e.g. an arm-watch). At least the motion recording part can be integrated with the arm-watch or in the sleeves of clothes, but the processing of the signal is conducted on a more powerful electronics such as mobile phone. Then, the devices can communicate via short range communication protocols (e.g. Bluetooth). Despite advantages, user authentication based on arm swing possess some limitations too. Several factors/situations may alter the natural arm swing, for example carrying an object in the hands, walking in various speeds, walking when one hand in the pocket another one is swinging and the like. For developing a robust user authentication system based on the arm swing, the effects of such factors and situations should be further investigated. 6 Conclusion and Future Work In this paper we investigated the feasibility of using arm swing during gait as a way of unobtrusive identity verifi- 1085

7 cation. User authentication based on the arm swing can also be suitable in continuous authentication context, where identity of the user is required to be re-verified periodically. Acceleration of the arm motion is collected using an accelerometer sensor. Using frequency domain analysis on the arm movement signals from 30 persons, we achieved the EER of about 10% and identification probability of 71.7% at rank 1. Although the results are encouraging, the further work is required to improve the accuracy of the approach and to evaluate performance on a larger data set (to have narrower confidence intervals). It is also important to study the factors that may influence the normal hand and arm movements (e.g. walking speed) in order to develop robust authentication system. For improving accuracy, one may look into developing techniques for integrating arm swing biometric with other unobtrusive biometrics such as hip movement and so on. In this paper, impostors were assumed passive, but in real life they can try to imitate the arm swing of a victim. Therefore, to evaluate the robustness of the system against such type of attack one needs to have active impostors too. All these open topics will constitute a basis for our future work. Acknowledgement We would like to thank Dr. Arne Wold for his useful discussion on the analysis of acceleration signals and Retoma AS for providing software interface between the sensor and the computer. References [1] B. Dukic and M. Katic. m-order - payment model via sms within the m-banking. In 27th International Conference on Information Technology Interfaces, [2] Mark S. Nixon, Tieniu N. Tan, and Rama Chellappa. Human Identification Based on Gait. Springer, [3] Davrondzhon Gafurov. A survey of biometric gait recognition: Approaches, security and challenges. In Annual Norwegian Computer Science Conference, Oslo, Norway, November [4] C. BenAbdelkader, R. Cutler, and L. Davis. Stride and cadence as a biometric in automatic person identification and verification. In Fifth IEEE International Conference on Automatic Face and Gesture Recognition, pages , May [5] Zongyi Liu and Sudeep Sarkar. Improved gait recognition by gait dynamics normalization. IEEE Transactions on Pattern Analysis and Machine Intelligence, 28(6): , [6] Ju Han and Bir Bhanu. Individual recognition using gait energy image. IEEE Transactions on Pattern Analysis and Machine Intelligence, 28(2): , [7] R. J. Orr and G. D. Abowd. The smart floor: A mechanism for natural user identification and tracking. In Proceedings of the Conference on Human Factors in Computing Systems, [8] J. Suutala and J. Röning. Towards the adaptive identification of walkers: Automated feature selection of footsteps using distinction sensitive LVQ. In Int. Workshop on Processing Sensory Information for Proactive Systems (PSIPS 2004), June [9] Lee Middleton, Alex A. Buss, Alex Bazin, and Mark S. Nixon. A floor sensor system for gait recognition. In Fourth IEEE Workshop on Automatic Identification Advanced Technologies (AutoID 05), pages , [10] Jam Jenkins and Carla Schlatter Ellis. Using ground reaction forces from gait analysis: Body mass as a weak biometric. In Pervasive, [11] Heikki J. Ailisto, Mikko Lindholm, Jani Mäntyjärvi, Elena Vildjiounaite, and Satu-Marja Mäkelä. Identifying people from gait pattern with accelerometers. In Proceedings of SPIE Volume: 5779; Biometric Technology for Human Identification II, pages 7 14, [12] Davrondzhon Gafurov, Einar Snekkenes, and Patrick Bours. Gait authentication and identification using wearable accelerometer sensor. In 5th IEEE Workshop on Automatic Identification Advanced Technologies (AutoID), pages , Alghero, Italy, June [13] Davrondzhon Gafurov, Einar Snekkenes, and Patrick Bours. Spoof attacks on gait authentication system. IEEE Transactions on Information Forensics and Security, 2(3), Special Issue on Human Detection and Recognition. [14] Davrondzhon Gafurov, Kirsi Helkala, and Torkjel Sondrol. Gait recognition using acceleration from MEMS. In 1st IEEE International Conference on Availability, Reliability and Security (ARES), pages , Vienna, Austria, April [15] R.M. Bolle, S. Pankanti, and N.K. Ratha. Evaluation techniques for biometrics-based authentication systems (FRR). In 15th International Conference on Pattern Recognition, pages , September

8 [16] ISO/IEC IS , information technology, biometric performance testing and reporting, part 1: Principles and framework, [17] Jani Mäntyjärvi, Mikko Lindholm, Elena Vildjiounaite, Satu-Marja Mäkelä, and Heikki J. Ailisto. Identifying users of portable devices from gait pattern with accelerometers. In IEEE International Conference on Acoustics, Speech, and Signal Processing, [18] Elena Vildjiounaite, Satu-Marja Mäkelä, Mikko Lindholm, Reima Riihimäki, Vesa Kyllönen, Jani Mäntyjärvi, and Heikki Ailisto. Unobtrusive multimodal biometrics for ensuring privacy and information security with personal devices. In Pervasive, pages , May Springer LNCS. [19] Donald M. Monro, Soumyadip Rakshit, and Dexin Zhang. DCT-Based iris recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 29(4), [20] U. Park, Pankanti, and A. K. Jain. Fingerprint verification using SIFT features. In SPIE Defense and Security Symposium, [21] D.Maio, D.Maltoni, R.Cappelli, J.L.Wayman, and A.K. Jain. FVC2002: Second fingerprint verification competition. In 16th International Conference on Pattern Recognition, [22] Xiangqian Wu, Kuanquan Wang, and David Zhang. Palmprint texture analysis using derivativeof gaussian filters. In International Conference on Computational Intelligence and Security, [23] Alisher Kholmatov and Berrin Yanikoglu. Identity authentication using improved online signature verification method. Pattern Recognition Letters,