FACE DETECTION. Lab on Project Based Learning May Xin Huang, Mark Ison, Daniel Martínez Visual Perception

Transcription

1 FACE DETECTION Lab on Project Based Learning May 2011 Xin Huang, Mark Ison, Daniel Martínez Visual Perception

2 Contents Introduction Exploration into local invariant features The final method Results Conclusions

3 Contents Introduction Exploration into local invariant features The final method Results Conclusions

4 Introduction Karlos Arguiñano s TV show.

5 Introduction Which is our goal?

6 Introduction Which is our goal? -> Detect presence of a face.

7 Introduction Problem definition

8 Problem definition: Introduction How can we use local invariant feature descriptors to detect Arguinano s face. Use MATLAB programming language.

9 Introduction Constraints of the project: Use local invariant features descriptors. Use MATLAB programming language.

10 Contents Introduction Exploration into local invariant features The final method Results Conclusions

11 Exploration into local invariant features First approaches: 1. SURF matching. 2. Template database SURF matching. 3. Clustering 4. Filtering best features. 5. SIFT matching

12 Exploration into local invariant features First approaches: 1. SURF matching. 2. Template database SURF matching. 3. Clustering 4. Filtering best features. 5. SIFT matching

13 Exploration into local invariant features 1. SURF matching: Speeded up robust features. Simplification of SIFT. Fast. Combination of novel detection, description, and matching steps. [ETH Swiss Federal Institute of Technlogy]

14 Exploration into local invariant features 1. SURF matching

15 Exploration into local invariant features 1. SURF matching Result with a rigid feature:

16 Exploration into local invariant features 1. SURF matching Result (no more fancy glasses):

17 Exploration into local invariant features First approaches: 1. SURF matching. 2. Template database SURF matching. 3. Clustering 4. Filtering best features. 5. SIFT matching.

18 Exploration into local invariant features 2. Template database SURF matching. Test image Classifier (NN) Output image with matchings FACE/NON FACE templates (SURF descriptors)

19 Exploration into local invariant features 2. Template database SURF matching +

20 Exploration into local invariant features 2. Template database SURF matching Result A

21 Exploration into local invariant features 2. Template database SURF matching Result B

22 Exploration into local invariant features First approaches: 1. SURF matching. 2. Template database SURF matching. 3. Clustering 4. Filtering best features. 5. SIFT matching.

23 Exploration into local invariant features 3. Clustering: Test image Classifier (NN) Output image with matchings FACE/NON FACE Clusters of SURF descriptors Clustering!

24 Exploration into local invariant features 3. Clustering: Ideal Reality

25 Exploration into local invariant features First approaches: 1. SURF matching. 2. Template database SURF matching. 3. Clustering 4. Filtering best features. 5. SIFT matching.

26 Exploration into local invariant features 4. Filtering best features: Find the extreme ratios. Two separate databases.

27 Exploration into local invariant features 4. Filtering best features: Blue BAD, Orange GOOD

28 Exploration into local invariant features SURF FAILS

29 Exploration into local invariant features SURF FAILS But...WHY?

30 Exploration into local invariant features SURF FAILS [REF] [A Comparison of SIFT, PCA-SIFT and SURF - Luo Juan, Oubong Gwun- International Journal of Image Processing- 2010]

31 Exploration into local invariant features First approaches: 1. SURF matching. 2. Template database SURF matching. 3. Clustering 4. Filtering best features. 5. SIFT matching.

32 Exploration into local invariant features 5. SIFT Matching: Test image Classifier (NN) Output image with matchings FACE/NON FACE Clusters of SIFT descriptors SIFT!

33 Exploration into local invariant features 5. SIFT Matching: Bringing in SIFT.

34 Exploration into local invariant features 5. SIFT Matching: We should do anything else...

35 Contents Introduction Exploration into local invariant features The final method Results Conclusions

36 1. Skin detection: The final method Help SIFT as much as possible. Make the ROI of skin color regions. Methodology: Extract skin segments: find the mean and covariance between CrCb.

37 1. Skin detection: The final method Methodology: Obtain the Gaussian probabilities of skin color pixels for the input image.

38 The final method 2. Skin detection: Methodology: Apply an adaptive threshold.

39 The final method 2. Skin detection: Methodology: Avoid region without holes and apply morphological operation over the binary image.

40 3. Face detection: The final method Filtering best features. Finding matches between each skin region and each template. D.Lowe

41 3. Face detection: The final method Check if each skin region is a face. Threshold = Face matches / No face matches

42 Contents Introduction Exploration into local invariant features The final method Results Conclusions

43 Results ans = TestImage_num: 1 Regioin_num: 1 Keypoint_num: 1135 TotalFaceMatch_num: 99 TotalNoFaceMatch_num: 14 TemplateMatchNum: [2x37 double] ans = TestImage_num: 1 Regioin_num: 2 Keypoint_num: 74 TotalFaceMatch_num: 3 TotalNoFaceMatch_num: 2 TemplateMatchNum: [2x37 double]

44 Results

45 Results

46 Results

47 Contents Introduction Exploration into local invariant features The final method Results Conclusions

48 Conclusions We have implement a face-detection method based on feature descriptors with a lot of help. SIFT over SURF QUALITY OF THE FEATURES over QUANTITY

49 Conclusions How can be improved? Use shape characteristics. A larger template database and a more robust classifier. Use morphology advantages. Apply other methods instead of feature description (i.e: HAAR). Use previous frames information.