Multi-view human facial image gender identification method and device

Abstract

The invention discloses a multi-view human facial image gender identification method and a device, wherein the method comprises the following steps: a step of matching by classifiers and a step of identifying human facial image gender. The step of matching by the classifiers comprises the following steps: S11, obtaining a plurality of sample human facial images from a human facial image library; S12, extracting sample characteristics of all sample human facial images and fuzzy matching the first classifier according to the sample characteristics; and S13, testing the sample characteristics by the first classifier and screening a target characteristic, and precisely matching the second classifier according to the target characteristic. The step of identifying human facial image gender comprises the following steps: S21, collecting video images of the target characteristic containing human faces to be detected; S22, extracting the target characteristic of the human faces to be detected; and S23, processing the target characteristic by the second classifier and identifying the human facial gender. The method and device disclosed by the invention can improve the identification rate of human facial images collected from different views.

Description

technical field [0001] The present invention relates to the technical field of image processing, in particular to a method and device for identifying the gender of a multi-view face image. Background technique [0002] Gender recognition in video images in the prior art can only recognize positive faces, and can only recognize one of the races. The current gender recognition technology mainly includes two aspects, one is the feature extraction method, and the other is the classification algorithm. [0003] Feature extraction methods include global feature extraction and local feature extraction. The global feature can reflect the overall composition of the image, and the purpose of global optimization can be achieved by analyzing the overall image. The methods of extracting global features include subspace analysis method, overall feature extraction method and so on. The global feature is mainly the overall statistical information of the face, mainly based on methods such...

AI Technical Summary

Problems solved by technology

This structure is essentially a degenerated decision tree. The subsequent strong classifier needs to make more complex decisions. When dealing with primary and secondary information, there is a certain false detection rate, which will not cause too much impact on the final result. However, for the binary classification problem of gender recognition, the importance of male attributes and female attributes is the same. If the previous decision is wrong, it will have a major impact on the final recognition results

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