Non-restraint face identification method based on HOG characteristic sparse representation

Abstract

The invention provides a non-restraint face identification method based on HOG characteristic sparse representation. According to the method, face database pictures are firstly inputted, and HOG characteristics of the inputted pictures are extracted; multiple pictures of each category of persons are selected for training, and other pictures are for tests; HOG characteristic column vectors of each training picture of each category of persons are utilized to construct a characteristic dictionary; a gradient projection sparse reconstruction algorithm is utilized to acquire HOG characteristic sparse representation coefficients of test samples; the sparse coefficients are sequentially kept according to categories, zero setting of the residual coefficients is carried out to acquire an approximate sparse coefficient, and the approximate sparse coefficient is multiplied by a dictionary to acquire a test sample estimate; a mean square error of the test sample and the estimate is calculated, a category of the test sample is determined according to the mean square error minimum principle. The method is advantaged in that influence of the non-restraint environment on face identification performance is effectively reduced, non-restraint face identification robustness is improved, a problem of slow operation speed caused by large dictionary dimensions existing in a traditional sparse representation classification algorithm is solved, and algorithm operation efficiency is effectively improved.

Description

technical field [0001] The invention relates to an unconstrained face recognition method based on HOG feature sparse representation. Background technique [0002] As one of the most potential biometric identification methods, face recognition has penetrated into all aspects of human daily life. Correctly identifying faces in an unconstrained environment is crucial to harmonious human-computer interaction. However, since unconstrained faces are affected by factors such as illumination, pose, occlusion, and resolution, it is a challenging task to design robust and efficient unconstrained face recognition methods. [0003] At present, the commonly used recognition methods are mainly divided into two categories: face recognition based on manual feature extraction and face recognition based on sparse representation. The face recognition method based on manual feature extraction is to manually select the face texture features according to the face interference factors, and then u...

AI Technical Summary

Problems solved by technology

[0008] The purpose of the present invention is to provide a non-constrained face recognition method based on HOG feature sparse representation to solve the difficulty of manually selecting the essential features of the face in the prior art, while the traditional dictionary is directly constructed on the basis of the original face image. The high dimensionality of the dictionary affects the efficiency of the algorithm, and the dictionary cannot describe the essential features, high redundancy, and poor sparsity

Images