One-sample face identification method

Abstract

The invention discloses a one-sample face identification method in the technical field of face identification. Firstly, a face image is pre-processed so as to obtain a single standard face image, and Shearlet transformation is conducted on the face image so as to obtain face frequency spectrum information of each sub-band; secondly, the face frequency spectrum information of all the sub-bands is fused and reconstructed to be combined into a fused image of the face; thirdly, according to information entropy, self-adaptive weighting is conducted on the fused image; fourthly, the frequency spectrum image is partitioned into sub-images, a classification result of each sub-image and a training sample is calculated, decisions are made, and a final classification and identification result is obtained. According to the one-sample face identification method, a complete and effective one-sample face identification solution is provided, influences caused by light, postures and other factors are avoided to a certain degree, the identification rate is high, and robustness is high.

Description

technical field [0001] The invention relates to the research fields of image processing and pattern recognition, in particular to a single-sample face recognition method. Background technique [0002] As an important biometric identification technology, face recognition aims to automatically identify the identity of the input person through the registered face database. At present, face recognition technology has been widely used in identity verification systems such as guard systems, public security department investigations, customs, and finance. However, many face recognition methods currently researched depend to a large extent on the number of training samples. In many practical applications, for example, in image data such as second-generation ID cards, driver's licenses, and passports, each person usually has only one training sample. , the same customs, airport monitoring and public security criminal identification are all single-sample identification, which makes t...

AI Technical Summary

Problems solved by technology

However, many face recognition methods currently researched depend to a large extent on the number of training samples. In many practical applications, for example, in image data such as second-generation ID cards, driver's licenses, and passports, each person usually has only one training sample. , the same customs, airport monitoring and public security criminal identification are all single-sample identification, which makes the existing identification methods unable to be practically applied

In addition, collecting multi-sample face databases in real face recognition is a time-consuming and labor-intensive project.

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