Abstract. Gender classification has found many useful applications in the broader domain of computer vision systems including in-cabin driver monitoring systems, human–computer interaction, video surveillance systems, crowd monitoring, data collection systems for the retail sector, and psychological analysis. In previous studies, researchers have established a gender classification system using visible spectrum images of the human face. However, there are many factors affecting the performance of these systems including illumination conditions, shadow, occlusions, and time of day. Our study is focused on evaluating the use of thermal imaging to overcome these challenges by providing a reliable means of gender classification. As thermal images lack some of the facial definition of other imaging modalities, a range of state-of-the-art deep neural networks are trained to perform the classification task. For our study, the Tufts University thermal facial image dataset was used for training. This features thermal facial images from more than 100 subjects gathered in multiple poses and multiple modalities and provided a good gender balance to support the classification task. These facial samples of both male and female subjects are used to fine-tune a number of selected state-of-the-art convolution neural networks (CNN) using transfer learning. The robustness of these networks is evaluated through cross validation on the Carl thermal dataset along with an additional set of test samples acquired in a controlled lab environment using prototype uncooled thermal cameras. Finally, a new CNN architecture, optimized for the gender classification task, GENNet, is designed and evaluated with the pretrained networks.

中文翻译：

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用于基于热图像的性别分类系统的最先进卷积神经网络的性能估计

摘要。性别分类在更广泛的计算机视觉系统领域中发现了许多有用的应用，包括车内驾驶员监控系统、人机交互、视频监控系统、人群监控、零售业数据收集系统和心理分析。在之前的研究中，研究人员利用人脸的可见光谱图像建立了性别分类系统。然而，影响这些系统性能的因素有很多，包括光照条件、阴影、遮挡和一天中的时间。我们的研究重点是评估使用热成像技术通过提供可靠的性别分类方法来克服这些挑战。由于热图像缺乏其他成像模式的某些面部清晰度，训练了一系列最先进的深度神经网络来执行分类任务。在我们的研究中，塔夫茨大学的热面部图像数据集用于训练。这具有来自 100 多个以多种姿势和多种方式收集的对象的热面部图像，并提供了良好的性别平衡来支持分类任务。这些男性和女性受试者的面部样本用于使用迁移学习对许多选定的最先进的卷积神经网络 (CNN) 进行微调。这些网络的稳健性通过对 Carl 热数据集的交叉验证以及使用原型非制冷热像仪在受控实验室环境中获取的一组额外测试样本进行评估。最后，一种新的 CNN 架构，针对性别分类任务进行了优化，GENNet，