Integrated convolutional neural network-based gender recognition method

Abstract

The invention discloses an integrated convolutional neural network-based gender recognition method. The method comprises the following steps: S1, first carrying out random combination to form a plurality of new training data sets, and selecting M convolutional neural network classifiers which are obtained through training by using the abovementioned new training data sets to serve as base classifiers; S2, obtaining a to-be-tested facial image; and S3, during the test, respectively inputting the to-be-tested facial image into the M base classifiers obtained in the step S1, and fusing gender types output by the M base classifiers to obtain a final gender type. According to the method disclosed by the invention, the convolutional neural network classifiers which are obtained through training by using the randomly obtained new training data sets serve as the base classifiers, the to-be-tested facial image is input into the M base classifiers, and finally the gender types output by the M base classifiers are fused to obtain the final gender type, so that the method has the advantages of being high in recognition accuracy rate, reducing the dependency, on the people, of the gender feature extraction of the facial image, and being wide in application.

Description

technical field [0001] The invention relates to the technical field of image processing, in particular to a gender recognition method based on an integrated convolutional neural network. Background technique [0002] Face gender recognition has great application value in personalized services, intelligent monitoring, etc. For example, in product recommendation, the product needs of men and women are very different. Facial gender recognition will help merchants understand the different preferences and needs of men and women, so as to improve products and provide better user experience and content quality. Face gender recognition can also help smart devices such as wearable devices to provide customized services, because different genders have different preferences, living habits and unique characteristics. [0003] At present, there are many face gender recognition methods, and the gender recognition system is generally divided into two parts: feature extraction of face imag...

AI Technical Summary

Problems solved by technology

This process relies heavily on the experience of human experts and repeated experiments. Not only is the workload heavy, but it is also difficult to find an optimal expression of facial gender characteristics, which affects the effect of facial gender recognition.

On the other hand, a single classifier such as a convolutional neural network as a face gender recognition method needs to adjust many parameters, parameter tuning is complicated, the workload is heavy, and it is difficult to obtain optimal parameters, thus affecting the overall face gender recognition performance

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