Human face identification method based on manifold learning

Abstract

The invention discloses a human face identification method based on manifold learning, and belongs to the technical field of image processing. The method solves the problem of excessive resource consumption of the traditional method for directly processing high-dimension images. The method is combined with two kinds of methods including the nearest characteristic sub space classifier method and the local linear embedding method for realizing the dimension reducing processing on human face images, then, the nearest classifier is adopted for identifying the data subjected to dimension reduction, firstly, the human face image high-dimension data is firstly built, and the human face image samples are stretched into one-dimension vectors in lines; then, the built human face image high-dimension data is subjected to dimension reduction processing, and the low-dimension expression of all obtained human face images is obtained; and finally, the data is embedded into the space at the low dimension. Through the training on the images, the images to be tested are collected in real time, the human face identification is carried out, the method is more reasonable than a local linear embedding method based on Euclidean distance, the identification accuracy is higher, the method has lower operation complexity than a method of directly adopting high-dimension data for identification, and the method is simpler and more convenient.

Description

technical field [0001] The invention belongs to the field of pattern recognition and computer vision, in particular to a face recognition method based on manifold learning. Background technique [0002] With the advent of the information technology era, effectively ensuring the security of personal information has become one of the hot issues in today's society. Reliable user identity authentication is a favorable guarantee for information security. However, traditional identity authentication methods are no longer applicable. development needs of today's society. Here, biometric authentication technology emerges as the times require, and the biometric features used for identification mainly include fingerprints, palmprints, irises, faces, auricles and other information. The many advantages of face recognition make it an indispensable part of biometric authentication technology. [0003] Manifold Learning, referred to as Manifold Learning, has become a research hotspot in ...

AI Technical Summary

Problems solved by technology

[0006] However, the above methods only meet the basic recognition rate requirements, and cannot meet the good recognition requirements, and need to improve the recognition rate more.

Images