Small-sample face recognition method

Abstract

The invention provides a small-sample face recognition method which relates to a method for face recognition by means of electronic equipment. According to the face recognition method, a single-layer multi-scale convolutional neural network structure is used for performing multi-characteristic fusion and classification. The method comprises the steps of preprocessing a face image; extracting a multi-characteristic graph of the face image; extracting a DWT low-frequency subband graph, an Sobel edge characteristic graph and an LBP pattern characteristic graph; performing multi-characteristic fusion by means of the single-layer multi-scale convolutional neural network structure; and predicating a classification result by means of a Softmax classifier, thereby realizing face recognition. The small-sample face recognition method overcomes a defect of low classification recognition rate caused by a classification method in settling a small-sample face recognition problem.

Description

technical field [0001] The technical solution of the present invention relates to a method for face recognition using electronic equipment, specifically a small-sample face recognition method. Background technique [0002] Face recognition technology, as an important branch of biometric recognition, has been developed for decades and has been widely used in various fields of people's lives. However, in practical applications, the lighting, Face recognition technology has always faced huge challenges such as pose, expression, and occlusion. In addition, many face recognition algorithms often require a huge network structure and a large amount of training data in practical applications, making it difficult to collect face samples. The resulting lack of training samples makes these face recognition algorithms fail to achieve their ideal recognition results in practical applications. [0003] In order to overcome the difficulty of collecting a large number of face samples, exis...

AI Technical Summary

Problems solved by technology

CN104268593A discloses a face recognition method in the case of a small sample. The method adopts the above-mentioned methods based on expanding virtual samples and data dimensionality reduction to solve the small sample problem. First, the virtual sample is constructed by mirror transformation, and then Three feature dimensionality reduction algorithms, KPCA, KDA, and KLLP, reduce the dimensionality of face images and construct a sparse representation model. Although feature dimensionality reduction can solve the problem of small samples, it is difficult to achieve a relatively high level of classification only through the error between models. high recognition rate

[0004] In the existing technology to solve the problem of small sample face recognition, limited by the classification method, the classification recognition rate is not high, so there is an urgent need for a method that can effectively solve the problem of small sample face recognition under the premise of ensuring the recognition rate

Images