A face attribute classification system based on a bidirectional Ladder structure

Abstract

The invention relates to the technical field of computer vision, in particular to a face attribute classification system based on a bidirectional Ladder structure, which aims to solve the problem of how to fully utilize characteristics of different levels in a deep network and corresponding relations between characteristics of different levels and different face attributes so as to improve the accuracy of face attribute classification. For this purpose, the face attribute classification system based on the bidirectional Ladder structure provided by the invention comprises a bidirectional Ladder self-encoder module, a self-adaptive attention module and a self-adaptive score fusion module, the bidirectional Ladder auto-encoder module comprises an encoder module and a decoder module, the self-adaptive attention module comprises a plurality of attention sub-modules, and the self-adaptive score fusion module is configured to obtain a face attribute classification result of the face image tobe detected according to an output result of the encoder module and a result output by the attention sub-module. Based on the structure, the coding features and the decoding features of different levels can be fully utilized, and the accuracy of face attribute classification is improved.

Description

technical field [0001] The invention relates to the technical field of computer vision, in particular to a human face attribute classification system based on a bidirectional Ladder structure. Background technique [0002] The face attribute classification method is a method for judging whether a given face image contains a certain face attribute, which is widely used in face recognition, face retrieval, face verification and other fields. At present, the use of deep network for face attribute classification can greatly improve the classification performance, but the deep network mainly focuses on the role of deep features on attribute classification, and shallow features have not been fully exploited and utilized. In addition, the corresponding relationship between the features of different layers of the deep network and different face attributes is not fully utilized, which greatly limits the accuracy of face attribute classification. [0003] Correspondingly, a new face ...

AI Technical Summary

Problems solved by technology

At present, the use of deep network for face attribute classification can greatly improve the classification performance, but the deep network mainly focuses on the role of deep features on attribute classification, and shallow features have not been fully exploited and utilized.

In addition, the corresponding relationship between the features of different layers of the deep network and different face attributes has not been fully utilized, which greatly limits the accuracy of face attribute classification.

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