US 9,430,697 B1Granted

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

AssigneeTcl Research America Inc.

InventorsMichael Iliadis, Armin Kappeler, Haohong Wang

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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.

Claims18

Dependent claims

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.

7 more elements

A face recognition system based on a computing device, comprising:

a storage module configured to store 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, and to store 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;
an acquisition module configured to obtain an input face image;
a computation module configured to:
partition the input face image into a plurality of blocks;
extract corresponding deep feature vectors of the plurality of blocks of the input face image using a deep learning network;
apply 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;
compute 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;
and
classify the input face image by selecting a face class that yields a minimum residual error as a recognition face class;
and
an output module configured to present the recognition face class of the input face image.

8 more elements

Dependencies are inferred from the claim text.