Gender recognition based on face image

Meiyan Zhang, Jinwei Sun, Dan Liu, Qisong Wang
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Abstract

The research on the biometrics recognition of facial image has made great progress. Compared with other biometrics, facial features are stable, simple, intuitive, friendly, non-intrusive, and easily accepted by people. Therefore, facial gender recognition technologies have been successfully applied to many commercial fields. However, the current gender recognition methods still have shortcomings such as low recognition rate and easily affected by surroundings. For this reason, a gender recognition method based on BP neural network is proposed. Firstly, this paper preprocesses the face images, extracts feature of face images, designs a BP neural network and uses the feature parameters to train BP neural network. Afterwards, a classifier based on the face image is obtained. Finally, the classifier is tested using pictures from the database, if demands are not met, BP neural network parameters would be redesigned and the training would be conducted on the BP neural network.
基于人脸图像的性别识别
人脸图像的生物特征识别研究取得了很大的进展。与其他生物识别技术相比,面部特征稳定、简单、直观、友好、非侵入性,容易被人们接受。因此,人脸性别识别技术已成功应用于许多商业领域。然而,目前的性别识别方法仍然存在识别率低、易受环境影响等缺点。为此,提出了一种基于BP神经网络的性别识别方法。首先,对人脸图像进行预处理,提取人脸图像特征,设计BP神经网络,并利用特征参数对BP神经网络进行训练。然后,得到基于人脸图像的分类器。最后,使用数据库中的图片对分类器进行测试,如果不符合要求,则重新设计BP神经网络参数,并在BP神经网络上进行训练。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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