Multi-task learning convolutional neural network-based face attribute analysis method

Abstract

The present invention discloses a multi-task learning convolutional neural network (CNN)-based face attribute analysis method. According to the method, based on a convolutional neural network, a multi-task learning method is adopted to carry out age estimation, gender identification and race classification on a face image simultaneously. In a traditional processing method, when face multi-attribute analysis is carried out, a plurality of times of calculation are required, and as a result, time can be wasted, and the generalization ability of a model is decreased. According to the method of the invention, three single-task networks are trained separately; the weight of a network with the lowest convergence speed is adopted to initialize the shared part of a multi-task network, and the independent parts of the multi-task network are initialized randomly; and the multi-task network is trained, so that a multi-task convolutional neural network (CNN) model can be obtained; and the trained multi-task convolutional neural network (CNN) model is adopted to carry out age, gender and race analysis on an inputted face image simultaneously, and therefore, time can be saved, and accuracy is high.

Description

technical field [0001] The invention relates to the field of image recognition, in particular to a face attribute analysis method based on a multi-task learning convolutional neural network. Background technique [0002] Traditional face image analysis techniques are often only for a single task, such as age estimation, gender recognition, race classification, etc. For multi-attribute analysis of faces, it needs to be calculated in multiple times, which is very time-consuming and difficult to meet actual needs. In addition, the single-task face image analysis technology ignores the connection between various information and cannot make full use of the information contained in the face image. The facial features of the human face are different between different genders and different races. For example, there are differences in skin fineness, skin color, and skin lightness between men and women, black and white, and skin lightness, color, and wrinkles. Texture, etc. will chan...

AI Technical Summary

Problems solved by technology

It can be seen that the information of each face is closely related, and learning each task independently will lose a lot of useful information to a certain extent, thereby reducing the generalization ability of the model.

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