Face recognition method for bottom-occluded face image

Abstract

The invention discloses a face recognition method for a bottom-occluded face image. Feature images are extracted for a to-be-recognized face image with bottom occlusion and a plurality of non-occludedface image samples respectively. According to the extraction method, a face region image is obtained through extraction and input into a convolutional neural network, feature maps output by the lastconvolutional layer are used as initial feature maps of the face image, each initial feature map is multiplied with a weight matrix to obtain one feature image, the values of weight elements corresponding to the upper portions of the initial feature maps in the weight matrix are large, the values of the weight elements corresponding to the lower portions of the initial feature maps are small, andthen the similarity between the feature image of the to-be-recognized face image and the feature image of each face image sample is calculated to complete face recognition. According to the method, byperforming region weight processing on the feature maps of the convolutional neural network, non-occlusion features are enhanced, and the face recognition rate of the bottom-occluded face image is increased.

Description

technical field [0001] The invention belongs to the technical field of face recognition, and more specifically, relates to a face recognition method for a lower occluded face image. Background technique [0002] Biometric identification technology is a technology for identity identification based on human biological characteristics. Commonly used biological characteristics include fingerprints, faces, irises, veins, etc. Compared with other recognition technologies, face recognition technology has the characteristics of good uniqueness, high acceptance, good concurrency, and easy promotion. Therefore, face recognition technology is widely used in various fields. [0003] Face recognition technology generally includes four steps, including face detection, face alignment, face expression and face classification. First detect the face ROI area in an image, and then perform face alignment according to the key point positioning of the face. After cropping, a relatively pure face...

AI Technical Summary

Problems solved by technology

However, these methods are generally traditional methods, which have certain limitations, such as little improvement in recognition rate, poor robustness to different occlusions, etc.

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