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

• People stand in front of a camera with good illumination conditions.

• Border pass, access control, attendance, etc.

Unconstrained Face Recognition

• Images are captured with less user cooperation, in more challenging conditions

• Video surveillance, hand held system, etc.

Partial Faces in Unconstrained Environments

Face Recognition and the London RiotsSummer 2011

Widespread looting and rioting:

Extensive CCTV Network:

FR lead to many arrests:

Yet, many suspects still unable to be identified by COTS FRS:

Face Detection in a Crowd

Normalized Pixel Difference (NPD) Face DetectorOpenCV Viola-Jones Face DetectorPittPatt-5 Face Detector

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

Alignment Free Partial Face Recognition (PFR)

• Proposed alignment-free method: MKD-SRC


• 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


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 of

in a probe image, solve

• Determine the identity of the probe image by SRC:

Sparse Representation Classification (SRC) based on MKD

An Example Solution

• 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

Morgan Freeman

Quincy Delight Jones

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.

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.

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

Keypoint Descriptors

SIFT(37)

GTP(first 150 of 571)

GTP Descriptor

• 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

Keypoint Region Normalization

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

• 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

Local Ternary Pattern

2201 2011 0222

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

GTP Descriptor

Local patch of 40x40 pixels4x4 grid cells34 bins for each cell

1296 bins in total PCA to 128 dims

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

1 http://vis-www.cs.umass.edu/lfw/

Experiments on LFW• MKD-SRC performs better than FaceVACS, but is not as

good as PittPatt• Fusion of MKD-SRC & PittPatt improves performance

Experiments on LFW

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

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/

Experiment on PubFig Database

• Proposed MKD-SRC method is better than two commercial SDKs, FaceVACS and PittPatt

Synthetic Partial Face Image Generation

5/28/2013 29

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

Rotated (size reduced for

display)

Original size patch

Rescaled patch

Original(size reduced for

display)

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

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

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

PittPatt cannot be applied because the probe faces cannot be aligned

Experiment on the Mobile dataset

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

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

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

Documents

Partial Face Recognition