标题： 探索深度学习技术在从眼图像中准确性别分类的可行性 显示英文标题

标题： Exploring the Feasibility of Deep Learning Techniques for Accurate Gender Classification from Eye Images

摘要： 性别分类已成为安全、人机交互、监控和广告等多个领域中的关键方面。 然而，这种分类的准确性可能受到化妆品和伪装等因素的影响。 因此，我们的研究致力于通过专注于使用眼周区域的颜色图像进行性别分类来解决这一问题。 眼周区域指的是眼睛周围的区域，包括眼睑、眉毛以及它们之间的区域。 它包含可用于提取用于性别分类的关键特征的有价值视觉线索。 本文介绍了一个复杂的卷积神经网络（CNN）模型，该模型利用颜色图像数据库来评估眼周区域在性别分类中的有效性。 为了验证模型的性能，我们在两个眼部数据集上进行了测试，即CVBL和（女性和男性）。 推荐的架构在之前未使用的CVBL数据集上达到了99%的优异准确率，而在（女性和男性）数据集上，使用少量可学习参数（7,235,089）也达到了令人称赞的96%的准确率。 为了确定我们提出的模型在使用眼周区域进行性别分类方面的有效性，我们通过广泛的指标评估了其性能，并与其他最先进的方法进行了比较。 结果明确表明了我们模型的有效性，从而表明其在安全和监控等领域的实际应用潜力。 显示英文摘要

摘要： Gender classification has emerged as a crucial aspect in various fields, including security, human-machine interaction, surveillance, and advertising. Nonetheless, the accuracy of this classification can be influenced by factors such as cosmetics and disguise. Consequently, our study is dedicated to addressing this concern by concentrating on gender classification using color images of the periocular region. The periocular region refers to the area surrounding the eye, including the eyelids, eyebrows, and the region between them. It contains valuable visual cues that can be used to extract key features for gender classification. This paper introduces a sophisticated Convolutional Neural Network (CNN) model that utilizes color image databases to evaluate the effectiveness of the periocular region for gender classification. To validate the model's performance, we conducted tests on two eye datasets, namely CVBL and (Female and Male). The recommended architecture achieved an outstanding accuracy of 99% on the previously unused CVBL dataset while attaining a commendable accuracy of 96% with a small number of learnable parameters (7,235,089) on the (Female and Male) dataset. To ascertain the effectiveness of our proposed model for gender classification using the periocular region, we evaluated its performance through an extensive range of metrics and compared it with other state-of-the-art approaches. The results unequivocally demonstrate the efficacy of our model, thereby suggesting its potential for practical application in domains such as security and surveillance.