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Partial Face Recognition

Partial Face Recognition. S. Liao, A. K. Jain, and S. Z. Li, "Partial Face Recognition: Alignment-Free Approach",  IEEE Transactions on Pattern Analysis and Machine Intelligence , Vol. 35, No. 5, pp. 1193-1205, May 2013, doi : 10.1109/TPAMI.2012.191. Cooperative Face Recognition.

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Partial Face Recognition

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  1. Partial Face Recognition S. Liao, A. K. Jain, and S. Z. Li, "Partial Face Recognition: Alignment-Free Approach", IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 35, No. 5, pp. 1193-1205, May 2013, doi: 10.1109/TPAMI.2012.191

  2. Cooperative Face Recognition • People stand in front of a camera with good illumination conditions. • Border pass, access control, attendance, etc.

  3. Unconstrained Face Recognition • Images are captured with less user cooperation, in more challenging conditions • Video surveillance, hand held system, etc.

  4. Partial Faces in Unconstrained Environments

  5. Face Recognition and the London RiotsSummer 2011 FR lead to many arrests: Widespread looting and rioting: Yet, many suspects still unable to be identified by COTS FRS: Extensive CCTV Network:

  6. Face Detection in a Crowd OpenCV Viola-Jones Face Detector PittPatt-5 Face Detector Normalized Pixel Difference (NPD) Face Detector

  7. Unconstrained Face Recognition • Problem: • Recognize an arbitrary face image captured in unconstrained environment • Possible areas for improvement: • Face detection? • Alignment? • Feature representation? • Classification? • Importance: • Recognize a suspect in crowd • Identify a face from its partial image

  8. Alignment Free Partial Face Recognition (PFR) • Proposed alignment-free method: MKD-SRC

  9. Alignment Free Partial Face Recognition (PFR) • Multi Keypoint Descriptors (MKD) • Each image is described by a set of keypoints and descriptors (e.g. SIFT): • Keypoints: p1, p2, …, pk • Descriptors: d1, d2, …, dk • The number of descriptors, k, may be different from image to image

  10. Alignment Free Partial Face Recognition (PFR)

  11. Sparse Representation Classification (SRC) based on MKD • Descriptors from the same class c can be viewed as a sub-dictionary: • Combining sub-dictionaries: • For each descriptor yi ofin a probe image, solve • Determine the identity of the probe image by SRC:

  12. Sparse Representation Classification (SRC) based on MKD

  13. An Example Solution Quincy Delight Jones Morgan Freeman • MKD-SRC is more discriminant for PFR • The horizontal axis represents the index of the gallery keypoint descriptors • The vertical axis denotes the coefficient strength, as computed by

  14. Large Scale Partial Face Recognition • In the dictionary, the number of atoms, K, can be of the order of millions • Fast atom filtering: (*) For each yi, we filter out only T (T<<K) atoms according to the top T largest values in ci, resulting in a small sub-dictionary. • The computation of Eq. (*) is linear w.r.t. K, the selection of the largest T values can be done in O(K), thus the proposed fast atom filtering scales linearly w.r.t. K, while the remaining computation of l1 minimization takes a constant time.

  15. Effects of the Fast Atom Filtering • A subset of FRGCv2, with 1,398 gallery images and 466 probe images, resulting in K=111,643 for the dictionary.

  16. Keypoint Descriptors • Scale Invariant Feature Transform (SIFT) • Advantage: promising results, efficient to compute • Disadvantage: limited number of keypoints (~80), not affine invariant • Gabor Ternary Pattern (GTP) descriptor • Adopts edge based affine invariant keypoint detector called CanAff, which provides sufficient number of keypoints (~800) for PFR • Robust to illumination variations and noises • Even with fast atom filtering, run time is O(n2) with keypoints per image • 10 times more keypoints, 100 times slower

  17. Keypoint Descriptors SIFT (37) GTP (first 150 of 571)

  18. GTP Descriptor

  19. Keypoint Region Normalization • Normalize the detected region to 40x40 pixels • Clipped Z-Score normalization: • Normalize the pixel values to [0,1] • Reduce the influence of illumination variation • Reduce the influence of extreme pixel values

  20. Gabor Filters • Odd Gabor filters with small scale, 4 orientations • Imaginary part of Gabor filters, sensitive to edges and their locations. • Scale 0, 5x5 support area, 0º, 45º, 90º, 135º

  21. Local Ternary Pattern • Encode the responses of the 4 Gabor filters • Local structure about the responses of Gabor filters in 4 orientations • Examples of some local structures encoded 4 orientations 2201 2011 0222

  22. Building the descriptor • Calculate the histogram of local ternary patterns (34 bins) over each grid cell, and concatenate them to form a 1,296 element vector • Transform by a sigmoid function ( tanh(20x) ) • Reduce the influence of extreme values • Reduce the dimension to 128 by PCA

  23. GTP Descriptor Local patch of 40x40 pixels 4x4 grid cells 34 bins for each cell 1296 bins in total PCA to 128 dims

  24. Labeled Faces in the Wild (LFW)1 • Real faces from the internet, most with non-frontal views or occlusion • 13,233 images of 5,749 subjects 1http://vis-www.cs.umass.edu/lfw/

  25. Experiments on LFW • MKD-SRC performs better than FaceVACS, but is not as good as PittPatt • Fusion of MKD-SRC & PittPatt improves performance

  26. Experiments on LFW Face image pairs that can be correctly recognized by MKD-SRC but not by PittPatt at FAR=1%

  27. Experiment on PubFig Database2 • Large-scale open-set identification • Gallery: 5,083 full frontal faces • Probe: • 817 partial faces (belong to gallery) with large pose variation or occlusion • 7,210 faces as impostors (do not belong to gallery) 2 http://www.cs.columbia.edu/CAVE/databases/pubfig/

  28. Experiment on PubFig Database • Proposed MKD-SRC method is better than two commercial SDKs, FaceVACS and PittPatt

  29. Synthetic Partial Face Image Generation • Rotate images; degree of rotation randomly drawn from a normal distribution (mean 0, std. dev. 10º) • Sample width and height for the patch, drawn from a uniform distribution from 50-100% of original size • Sample a starting position for the patch • Randomly rescale the patch Original (size reduced for display) Rotated (size reduced for display) Rescaled patch Original size patch

  30. FRGC+ Dataset • Open set recognition • FRGC dataset • Gallery: • 466 FRGC Images • 10,000 PCSO Images • Probe • A. 15,562 FRGC partial faces (matching the FRGC subjects in gallery) • B. 10,000 PCSO partial faces (not matching any gallery subjects) • Average time per probe image ~1 second vs. 10,466 image gallery • Pittpatt 5.2 fails to enroll ~50% of the partial faces

  31. Experiment on MOBIO database3 • Videos captured by mobile phone from six universities/institutes in Europe • 4,880 videos of 61 subjects for verification Gallery (top) and probe (bottom) 3 http://www.idiap.ch/dataset/mobio

  32. Experiment on MOBIO database A. Female B. Male

  33. Experiment on the Mobile dataset • Unconstrained face images with a mobile phone • Pose, illumination, expression, occlusion or invisible parts • Gallery images of 14 subjects plus additional 1,000 background subjects; one image/subject • Probe: 168 mobile phone images of 14 subjects, with additional 1,000 impostors • Open-set (watch-list) identification experiment

  34. Experiment on the Mobile dataset PittPatt cannot be applied because the probe faces cannot be aligned

  35. Other Keypoint Matching Methods • Keypoint based representations are naturally variable size • The previously discussed method reconstructs each probe keypoint from the gallery using SRC • Other options: • Bag of words methods – fixed sized representation over a dictionary • Modified Hausdorff Distance – apply a general distance metric to sets of points

  36. Modified Hausdorff Distance • Given a distance metric d, and 2 sets of keypoints A and B find: • D(A,B) = mean(mina in A(d(a,B))) • Compute the min distance from each keypoint in A to a keypoint in B, average the results over all keypoints in A • D(A,B) ≠ D(B,A) • MHD(A,B) = max(D(A,B), D(B,A)) • We calculate all probe to gallery keypoint distances for the atom filtering step, so computing MHD is not costly

  37. Summary • Face recognition based on applying SRC to local keypoint descriptors • Outperformed by other methods for mugshot style images, but can be used even when faces cannot be aligned • E.g. only part of the face is available, or face/eye detection fail

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