基于神经网络的全脸照片性别检测算法

Elham Arianasab, M. Maadani, Abolfazl Gandomi
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引用次数: 4

摘要

性别检测在智能广告和身份验证等应用中越来越受到关注。目前可用的高处理能力使得高进程消耗任务成为可能。本文研究了基于18岁以上人群正面面部图像的性别检测。首先对人脸图像的特征提取进行了研究,介绍了人脸图像的最佳特征。提出了一种基于神经网络的性别检测方法,并收集了200名女性和200名男性的数据库,对神经网络进行训练和测试。为了消除对种族和族裔的限制,这个数据库是根据一些标准的伊朗图片制作的,关于这个问题已有标准数据库。与以往的研究相比,该方法的实施结果表明,其精度有了明显的提高。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A neural-network based gender detection algorithm on full-face photograph
Gender detection is gaining much more interest for applications such as smart advertisement and authentication. High processing-power available today, makes possible the high process-consuming tasks. In this paper, gender detection of over 18 years old persons is investigated based on their frontal facial images. First the feature-extraction is studied and the best features in a facial image are introduced. A Neural Network (NN) based method is developed for gender detection and a database of 200 females and 200 males is gathered for training and testing the neural network. To eliminate restrictions on race and ethnicity, the database is produced based on some standard Iranian pictures and standard databases exist on this subject. The result of implementation of the proposed method shows completely a noticeable improvement in accuracy, compared to previously performed researches.
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