Face authentication method and device

Abstract

The invention discloses face authentication method and device, and belongs to the field of biological recognition. The method comprises the following steps: sequentially extracting multiple levels of feature vectors from a to-be-authenticated face image and a face image template with a multi-level depth convolutional network which is subjected to multi-layer classified network joint training in advance; sequentially mapping the multiple levels of feature vectors into unified dimension feature vectors through a unified dimension linear mapping matrix; connecting the unified dimension feature vectors into joint feature vectors in series; carrying out dimension-reducing mapping on the joint feature vectors through a linear dimension-reducing mapping matrix; and normalizing cosine values with absolute values through linear discriminant analysis, and carrying out comparison and authentication on the obtained comprehensive feature vectors of the to-be-authenticated face image and comprehensive feature vectors of the face image template. Compared with the prior art, the face authentication method disclosed by the invention is high in anti-jamming capability, good in expandability and high in authentication accuracy rate.

Description

technical field [0001] The invention relates to the field of biometric identification, in particular to a method and device for face authentication. Background technique [0002] Face authentication is a form of biometric identification. By effectively characterizing the face, the features of two face pictures are obtained, and a classification algorithm is used to determine whether the two pictures are the same person. Generally, a face image is pre-stored in the face recognition device as a face image template; during authentication, a face image is taken as a face image to be authenticated, the features of the two images are extracted, and classification algorithms are used to Determine if the two photos are of the same person. [0003] The method of feature extraction is: manually design a feature vector, and extract the specified feature vector through various algorithms, such as face authentication methods based on geometric features, face authentication methods based...

AI Technical Summary

Problems solved by technology

[0003] The method of feature extraction is: manually design a feature vector, and extract the specified feature vector through various algorithms, such as face authentication methods based on geometric features, face authentication methods based on subspace, and face authentication methods based on signal processing. etc., but this method is extremely susceptible to the influence of lighting, expression and other factors on the results, and has poor anti-interference ability, and the artificially designed feature vectors are mostly based on specific situations, and the scalability is poor.

[0004] Face recognition and authentication technology based on deep network can automatically learn and extract features, but the general deep network has the problem of gradient dispersion, and the processing and understanding of features at each level is insufficient, and only using high-level features is not enough to fully describe the image

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