Face verification method and system

Abstract

The present invention relates to a face verification method and system. The method includes: using principal component analysis and linear discriminant analysis to respectively preprocess high-dimensional face feature data, including setting the data dimension after principal component analysis dimension reduction; establishing a discriminant High-order Boltzmann machine, set the number of nodes in the hidden layer; use tensor diagonalization strategy to reduce the model parameters of the discriminative high-order Boltzmann machine; input the paired face data into In the discriminative high-order Boltzmann machine, the stochastic gradient descent algorithm is used to maximize the conditional probability of the relationship category, thereby iteratively optimizing the weight of the Boltzmann machine, so as to obtain the final discriminative high-order Boltzmann machine; input the paired face data to be verified into the discriminant high-order Boltzmann machine model, and obtain the corresponding verification result data. The invention enhances the discriminative power of the model by introducing the data relationship category information into the unsupervised Boltzmann machine model, and is more suitable for face verification with precision requirements.

Description

technical field [0001] The invention relates to a face verification method and system, in particular to a face verification method and system based on a discriminant high-order Boltzmann machine. Background technique [0002] Face verification technology has been a hot research issue in the field of pattern recognition due to its wide range of applications, such as identity authentication in customs and banks. [0003] Given a pair of face images, the goal of face verification is to determine whether the two face images represent the same person. The previous work mainly focused on face verification in a constrained environment, that is, the face images in the experiment were collected under controlled environmental factors, including fixed lighting, posture and expression. However, in practical applications, these factors are uncontrollable. For example, face images in video surveillance may contain arbitrary lighting. Therefore, many researchers have recently turned to t...

AI Technical Summary

Problems solved by technology

Face images under unconstrained environmental factors have very large intra-class variance, which will be challenging to deal with

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