Human face recognition method taking convolutional neural network as feature extractor

Abstract

The invention discloses a human face recognition method taking a convolutional neural network (CNN) as a feature extractor. The method is characterized by comprising the following steps of SS1: constructing a to-be-identified human face image database according to a recognition need, and forming a training set and a test set according to needs; SS2: extracting a CNN model as the feature extractor; SS3: extracting to-be-identified eigenvectors of images of the to-be-identified human face library by the CNN model, and performing storage; and SS4: classifying extracted training set and test set data by utilizing a feature classifier. According to the method, a relatively popular deep learning CNN in existing image recognition serves as the feature extractor, so that the characteristics of high recognition rate and high anti-jamming property of the CNN can be utilized, the to-be-identified human face database can be conveniently subjected to modification operation, and the practicality of the human face recognition method in the field of human face identity recognition is improved.

Description

technical field [0001] The invention relates to a face recognition method using a convolutional neural network as a feature extractor, and belongs to the technical field of personal identity recognition. Background technique [0002] Face recognition is a kind of biometric identification technology based on human facial feature information. Use a camera or camera to collect images or video streams containing human faces, and automatically detect and track human faces in the images, and then perform a series of face-related technologies on the detected human faces. At present, face recognition technology has been widely used in the fields of finance, justice, military, public security, border inspection, government, aerospace, electric power, factories, education, medical care, and many enterprises and institutions, and has become a hot research topic for many enterprises. direction, and look forward to its greater role in more application fields. [0003] Traditional face ...

AI Technical Summary

Problems solved by technology

[0003] The traditional face recognition method is based on the traditional feature extraction and feature classification methods: for example, the popular Eigenface method is based on the principal component analysis (PCA) method to classify the face images to be recognized, but these features The classification method has a key problem: that is, insufficient stability, and it is difficult to realize multi-scene, multi-environment and multi-pose face recognition. Although the emerging face recognition method based on deep learning can solve the aforementioned problems, it is not suitable for the application. The framework has high requirements, and it is difficult to achieve good environment transplantation and the effect of database addition and deletion

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