Small sample face recognition method combining sparse representation and neural network

Abstract

The invention discloses a small-sample face recognition method combining sparse representation and a convolutional neural network, and the method comprises the following steps: firstly carrying out the preprocessing of a face image, carrying out the face alignment and five-sense-organ positioning according to face key points, and cutting the face image into four local regions; extacting local features and overall features with higher discrimination by using a convolutional neural network, and constructing a block feature dictionary in combination with a sparse representation algorithm, so as to achieve the effect of sample enhancement; adding sparse representation constraints and cosine similarity to redefine a loss function of the convolutional neural network so as to reduce the intra-class distance between the features and expand the inter-class distance; and finally, carrying out face recognition by adopting enhanced sparse representation classification. The method is high in recognition performance, and has certain robustness for shielding changes in a small sample problem.

Description

technical field [0001] The invention belongs to the technical field of face recognition, and in particular relates to a small-sample face recognition method combined with a sparse representation neural network, and is especially suitable for scenes with large changes in face images such as occluded expressions. Background technique [0002] In modern society, identity authentication has important applications in many occasions, such as public security criminal investigation, social life services, and Internet finance. Recognition technology mainly includes methods based on human biological characteristics such as fingerprints, irises, and faces, and face recognition has a broader application prospect because of its natural and friendly advantages. In actual situations, the face recognition system often encounters the problem of small sample face recognition, that is, only one or a few samples are stored in the face database for each person. This is because the number of face...

AI Technical Summary

Problems solved by technology

In actual situations, the face recognition system often encounters the problem of small sample face recognition, that is, only one or a few samples are stored in the face database for each person. This is because the number of face samples that can be collected in real situations is relatively small. Less, and less training samples will also affect its recognition effect

[0003] Usually, the collection environment of face images is carried out in an uncontrollable natural environment, and face samples often contain changes such as illumination, posture, occlusion, expression, noise, etc., and these changes will affect the recognition of the system to a certain extent Accuracy

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