A Small Sample Face Recognition Method

Abstract

The invention relates to a small-sample face recognition method, which relates to a face recognition method using electronic equipment, and is a face recognition method that uses a single-layer multi-scale convolutional neural network structure to perform multi-feature fusion and classification. The steps are: Face image preprocessing; extraction of multi-feature maps of face images: including extracting a layer of DWT low-frequency sub-band maps, extracting Sobel edge feature maps and extracting LBP texture feature maps; using a single-layer multi-scale convolutional neural network structure for multi-feature fusion ; Use the Softmax classifier to predict the classification results and realize face recognition. The invention overcomes the defect that the classification recognition rate is not high due to the limitation of the classification method in solving the problem of small-sample face recognition in the prior art.

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

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