SVD (Singular Value Decomposition)-based method for extracting joint features of multi-source face images

Abstract

The invention aims to provide an SVD (Singular Value Decomposition)-based method for extracting joint features of multi-source face images so as to increase the recognition rate of face recognition. The method comprises the following steps of: A, extracting the grayscale image, the binary image and the intuitive feature image of a human face sample to serve as data sources and merging into a jointfeature; B, extracting the attribute value of the joint feature, calculating a reverse integral image, carrying out singular value decomposition on the reverse integral image to obtain the singular value of the reverse integral image and calculating a singular value matrix of the reverse integral image by utilizing the singular value of the reverse integral image; C: accelerating the feature calculation by utilizing the singular value matrix of the reverse integral image and trilinear interpolation to obtain a high-dimensional directional gradient histogram; and D: carrying out feature dimension reducing calculation on the directional gradient histogram by utilizing a local mean-based kernel nearest neighbor convex hull algorithm to obtain a low-dimensional face image directional gradienthistogram feature.

Description

Technical field [0001] The invention relates to a method for extracting features of a face image. Background technique [0002] In the rapid development of modern science and technology, personal identification technology is of great significance in the fields of finance, security, justice, and investigation. Due to the comprehensive popularization of network technology, information security has also shown unprecedented importance. In security, judicial and other fields, accurate identification is required. The technology of identifying personal identity through image processing and pattern recognition has the advantages of strong reliability, fast recognition speed, convenient method, low price, good naturalness and acceptability, etc., so it has become the focus of technology developers , Used in various fields. However, with the development of science and technology in society, people's needs no longer stay in functional requirements, but more are the pursuit of efficient an...

AI Technical Summary

Problems solved by technology

[0003] A single sample cannot clearly and accurately represent the information of the current face image. For the same face, different positions, lighting, and angles can form a series of different images, and such images represent the same face. , it is obviously impossible to accurately express the face information

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