Cross-pose face recognition method based on progressive neural network and attention mechanism

Abstract

The invention discloses a cross-pose face recognition method based on a progressive neural network and an attention mechanism. The cross-pose face recognition method comprises the following steps: performing face detection and key point alignment by using an MTCNN tool; estimating attitude information of each face image; sampling a front face image for each face image to serve as a reference image, forming image pairs, inputting the image pairs into a designed progressive neural network, extracting face feature pairs, and performing identity prediction; calculating an attention weighted mean square error loss function by using the feature pairs, and training a neural network in combination with a classified cross entropy loss function; and extracting face features by using the trained neural network and performing face verification. According to the invention, a new lightweight progressive network structure and an attention-based loss function are constructed, and the features of the side face image can be effectively adjusted in a feature space, so that the problem that the face recognition performance is reduced due to posture change in the face recognition field is solved.

Description

technical field [0001] The invention relates to the technical field of face recognition, in particular to a cross-pose face recognition method based on a progressive neural network and an attention mechanism. Background technique [0002] Face recognition is a key technology that is widely used in banking, e-commerce, Internet finance, electronic entertainment, education, finance, securities and other fields. However, although the current face recognition technology has achieved remarkable recognition accuracy, when dealing with faces with large poses, the recognition accuracy of most face verification algorithms tends to drop compared to faces with smaller poses. very obvious. Senguptal et al pointed out in the 2016 IEEE WACV conference article that the recognition accuracy of these algorithms dropped by more than 10% when recognizing large pose faces. This means that the impact of face posture changes has become an important challenge in the field of face recognition, an...

AI Technical Summary

Problems solved by technology

Although this can flexibly deal with faces of different poses, the use of multiple deep neural networks undoubtedly doubles the amount of parameters and calculations.

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