Face recognition method for lower occluded face images

Abstract

The invention discloses a face recognition method for face images with lower occlusions. For face images to be recognized with lower occlusions and several face image samples without occlusions, feature images are extracted respectively. The extraction method is as follows: extracting and obtaining The image of the face area is input into the convolutional neural network, and the feature map output by the last layer of convolution layer is used as the initial feature map of the face image, and each initial feature map is multiplied by the weight matrix to obtain the feature image. The value of the weight element corresponding to the upper part of the initial feature map in the matrix is ​​larger, and the value of the weight element corresponding to the lower part of the initial feature map is smaller, and then calculate the feature image of the face image to be recognized and the weight of each face image sample The similarity of feature images completes face recognition. In the present invention, the area weight processing is performed on the feature map of the convolutional neural network, so as to strengthen the unoccluded feature and improve the face recognition rate of the lower occluded face image.

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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