Method and system for face recognition using deep collaborative representation-based classification

Patent metadata

Field	Value
Publication number	US-9430697-B1
Application number	US-201514791388-A
Country	US
Kind code	B1
Filing date	Jul 3, 2015
Priority date	Jul 3, 2015
Publication date	Aug 30, 2016
Grant date	Aug 30, 2016

Abstract

The present invention provides a face recognition method. The method includes obtaining a plurality of training face images which belongs to a plurality of face classes and obtaining a plurality of training dictionaries corresponding to the training face images. A face class includes one or more training face images. The training dictionaries include a plurality of deep feature matrices. The method further includes obtaining an input face image. The input face image is partitioned into a plurality of blocks, whose corresponding deep feature vectors are extracted using a deep learning network. A collaborative representation model is applied to represent the deep feature vectors with the training dictionaries and representation vectors. A summation of errors for all blocks corresponding to a face class is computed as a residual error for the face class. The input face image is classified by selecting the face class that yields a minimum residual error.

First claim

What is claimed is:

1. A face recognition method on a computing device, comprising:
obtaining a plurality of training face images which belongs to a plurality of face classes, wherein a face class includes one or more training face images and represents an identification of the one or more training face images;
obtaining a plurality of training dictionaries corresponding to the plurality of training face images, wherein the plurality of training dictionaries include a plurality of deep feature matrices;
obtaining an input face image;
partitioning the input face image into a plurality of blocks;
extracting corresponding deep feature vectors of the plurality of blocks of the input face image using a deep learning network;
applying a collaborative representation model to represent the deep feature vectors of the blocks of the input face image with the training dictionaries and representation vectors;
computing residual errors for the face classes, a residual error for a face class being a summation of errors for all blocks corresponding to the training face images in the face class, wherein an error for a block exists between a feature vector of the block in the input face image and the collaborative representation model of the block corresponding to the face class;
classifying the input face image by selecting a face class that yields a minimum residual error as a recognition face class; and
presenting the recognition face class of the input face image.

2. The face recognition method according to claim 1 , wherein:
the training face images are partitioned into a plurality of non-overlapping blocks;
a deep feature matrix is extracted from blocks at a same location in the training face images using the deep learning network; and
the plurality of blocks of the input face image are non-overlapping.

3. The face recognition method according to claim 1 , wherein:
the deep learning network is a convolutional neural network; and
the convolutional neural network includes at least five convolutional layers, max-pooling layers and two fully connected layers.

4. The face recognition face recognition method according to claim 1 , wherein:
obtaining an input face image further includes:
dividing an input video into different sets of frames;
detecting faces of each frame in the input video;
generating face tracks for the whole video, a face track being a group of detected faces within a same camera take; and
obtaining detected faces and face tracks information, wherein the input face image is obtained from the detected faces; and

presenting the face class of the input face image further includes outputting a video by adding annotations about the face classes of the detected faces in the input video according to the face tracks.

5. The face recognition method according to claim 1 , further comprising:
storing user preference information along with the training dictionaries corresponding to one or more users; and
after obtaining the face class of the input face image, recommending personalized contents to a user corresponding to the face class according to the user preference information.

6. The face recognition method according to claim 1 , wherein:
provided that T denotes the training face images, y denotes the input face image, the training face images and the test face image are partitioned into p blocks, F(•) denotes deep features extracted from blocks in training face images, F(T r ) denotes the deep feature matrix of a r th block in the training face images, F(T i r ) is a submatrix of F(T r ) and denotes the deep feature for the r th block in a i th face class, f(y r ) denotes the deep feature vector of the r th block from the input face image, a*r denotes a resultant representation vector of the r th block obtained from the collaborative representation model, w* r denotes a weight corresponds to the resultant representation vector a* r , a* i r denotes a segment of a resultant representation vector a* r associated with class i, and e i (y) denotes the residual error for the class i,
the residual for the face class i is computed as e i (y)=Σ r=1 p w* r |f(y r )−F(T i r )a* i r ∥ 2 2 ; and
the identity of y is given as, Identity(y)=argmin i {e i }.

7. The face recognition method according to claim 1 , wherein
provided that T denotes the training face images, y denotes the input face image, the training face images and the test face image are partitioned into p blocks, F(®) denotes deep features extracted from blocks in training face images, F(T r ) denotes the deep feature matrix of a r th block in the training face images, f(y r ) denotes the deep feature vector of the r th block from the input face image, a r denotes a representation vector of the r th block, and a* r denotes a resultant representation vector of the r th block,
the representation vector is obtained by solving a relaxed collaborative representation problem

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Assignees

• Tcl Res America Inc

Inventors

• Iliadis Michael
• Kappeler Armin
• Wang Haohong

Classifications

• G06V10/772

  Determining representative reference patterns, e.g. averaging or distorting patterns; Generating dictionaries · CPC title

• G06V20/49 Primary

  Segmenting video sequences, i.e. computational techniques such as parsing or cutting the sequence, low-level clustering or determining units such as shots or scenes · CPC title

• G06V40/172

  Classification, e.g. identification · CPC title

• G06F18/28

  Determining representative reference patterns, e.g. by averaging or distorting; Generating dictionaries · CPC title

• G06F16/53

  Querying · CPC title