Deep learning human face identification method based on weighting L2 extraction

Abstract

The invention discloses a deep learning human face identification method based on weighting L2 extraction. According to the method, firstly, the human face feature vector is extracted through various-convolution-kernel convolution, then, a weighting L2 extraction method is utilized for carrying out dimensionality reduction on the feature vector, and then, a local average normalizing processing method is adopted for normalizing the feature vector, so a layer of network in the deep learning is formed, the same method is used for building three layers of deep leaning networks, in addition, the three layers of deep learning networks are subjected to cascade connection for forming a layered three-layer deep learning network, and finally, a support vector machine classifier is utilized for carrying out human face training and identification. The deep learning human face identification method has the advantages that the weighting L2 extraction method is provided for realizing the feature dimensionality reduction, the over fitting problem in the training and the single feature problem in the traditional L2 extraction are solved, the feature vector dimensionality reduction is effectively realized, meanwhile, the human face identification performance can be improved, higher grade of features can be effectively extracted, the stability is high, and the identification performance is high.

Description

technical field [0001] The invention relates to a face recognition method, in particular to a face recognition method based on weighted L2 extraction deep learning. Background technique [0002] Face recognition technology refers to a technology that identifies whether a static image or an image in a dynamic video is a human face. Face recognition technology belongs to biometric identification technology, which specifically refers to computer technology that uses analysis and processing of face visual feature information for identity identification. The human face is the most important and direct carrier for the expression and communication of human emotions. Through the human face, information such as a person's race, region, and even identity and status can be inferred. Since the late 1990s, some commercialized face recognition systems have gradually entered the market. Commonly used applications such as national security, military security, and public security, smart acc...

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Problems solved by technology

[0004] In order to overcome the above-mentioned shortcomings and deficiencies of the prior art, the object of the present invention is to provide a face recognition method based on weighted L2 extraction deep learning, which overcomes the singleness of traditional L2 extraction features and the overfitting problem in training, and improves the face recognition technology. recognition performance

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