Deep learning face identification method based on multiple-characteristic fusion

Abstract

The invention discloses a deep learning face identification method based on multiple-characteristic fusion. The method comprises the steps of firstly performing 2D gabor transforming on a to-be-tested orl face database and extracting the face database with a gabor characteristic; because of overlarge dimension of the picture being 92*112, reducing the dimension of the image to 32*32 according to a bilinear interpolation method; then fusing the original orl face database with the face database with the gabor characteristic; and finally coding in a stack type self-coding manner in deep learning, and calculating a weight parameter in a sofmax regression manner, and predicting an identification accuracy. According to the deep learning face identification method, after multiple characteristics are fused under a precondition that the testing accuracy of the characteristic through singly utilizing the algorithm is not lower than 80%, higher accuracy and high algorithm stability are realized; and even after random initialization, identification accuracy is basically unchanged.

Description

technical field [0001] The invention belongs to the technical field of image recognition and deep learning, and relates to a learning face recognition method, in particular to a deep learning face recognition method based on multi-feature fusion. Background technique [0002] Face recognition is a biometric technology for identity authentication based on human facial feature information. Capture images or video streams containing human faces through cameras or cameras, and automatically detect and track human faces in the images, and then match and recognize detected faces. [0003] Face recognition has a wide range of applications, especially plays a very important role in many fields such as security and anti-terrorism, financial payment, access control attendance, identity recognition, etc. It involves domain knowledge such as biomedicine, pattern recognition, image processing, machine study etc. [0004] Face recognition algorithms mainly include: [0005] ①Template m...

AI Technical Summary

Problems solved by technology

The main problem with this method is that if the data set is large enough, it will take a lot of time to match and the accuracy will be reduced.

Overemphasizing the large distance between classes and ignoring the features with small distance between classes will eventually cause a large number of overlapping categories with small distance between classes, resulting in low final recognition accuracy

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