Face Recognition Method Based on Combining Face Attribute Information

Abstract

The invention discloses a face recognition method based on combining face attribute information, and belongs to the technical field of digital image processing. Aiming at the technical problem that the existing fusion method needs to train multiple DCNN networks, and then performs score fusion or feature fusion for further training, the work task is heavy and complex, which is not conducive to practical application, and discloses a new fusion method of identity information and attribute information. way to improve the accuracy of face recognition. The invention fuses the face identity authentication network and the attribute recognition network to form a fusion network, and adopts the joint learning method to learn the identity feature and the face attribute feature at the same time, which not only improves the correct rate of face recognition, but also can predict the face. It is a multi-task network; it adopts a cost-sensitive weighting function, so that it does not depend on the data distribution of the target domain, and realizes balanced training in the source data domain; and the modified fusion framework only adds a small number of parameters, The additional computational load is relatively small.

Description

technical field [0001] The invention belongs to the technical field of digital image processing, and in particular relates to a face recognition method based on combining face attribute information. Background technique [0002] With the rapid development of deep learning, face recognition technology has developed by leaps and bounds, and many products using face recognition technology have emerged as the times require. However, the current face recognition technology still has many limitations. Among them, the very typical problems are the influence of environmental factors such as large side faces and lighting, which will reduce the performance of the face recognition system. Many researchers have done a lot of work on face pose correction and domain adaptation. Although they have achieved many results, they are still in the exploratory stage. After research, it is found that many face attribute information (such as: gender, eyebrow shape, nose bridge height, etc.) are no...

AI Technical Summary

Problems solved by technology

[0007] The above two methods need to train multiple DCNN networks, and then perform score fusion or feature fusion for further training. The tasks are heavy and complicated, which is not conducive to practical application.

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