用眼周区域图像实现99%准确率的性别分类。
Exploring the Feasibility of Deep Learning Techniques for Accurate Gender Classification from Eye Images
- 构建专用CNN模型，分析眼周区域颜色图像特征。
- 在CVBL数据集上达99%准确率，参数仅720万。
- 适合安防、人机交互等需要高精度性别识别场景。
性别分类在安全、人机交互、监控和广告等领域日益重要。然而，化妆和伪装等因素会影响分类准确性。为此，本研究聚焦眼周区域（包括眼睑、眉毛及二者间区域）的颜色图像，利用其中蕴含的视觉线索进行性别分类。本文提出一种先进的卷积神经网络（CNN）模型，基于两个眼区数据集——CVBL与（Female and Male）——评估其有效性。在未使用过的CVBL数据集上，模型达到99%的准确率；在（Female and Male）数据集上，以7,235,089个可学习参数实现96%的准确率。通过多种指标对比，结果明确证明该模型在眼周区域性别分类上的优越性，具备在安全与监控等领域的实际应用潜力。
原文摘要 · Abstract (English)
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.
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