This paper describes the investigation results about the usage of shallow (limited by few layers only) convolutional neural networks (CNNs) to solve the video-based gender classification problem. Different architectures of shallow CNN are proposed, trained and tested using balanced and unbalanced static image datasets. The influence of diverse voting over confidences methods, applied for frame-by-frame gender classification of the video stream, is investigated for possible enhancement of the classification accuracy. The possibility of the grouping of shallow networks into ensembles is investigated; it has been shown that the accuracy may be more improved with the further voting of separate shallow CNN classification results inside an ensemble over a single frame or different ones.

中文翻译：

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使用 Shallow CNN 的视频流性别分类

本文描述了使用浅层（仅限于几层）卷积神经网络（CNN）解决基于视频的性别分类问题的调查结果。使用平衡和不平衡的静态图像数据集提出、训练和测试了不同的浅层 CNN 架构。研究了应用于视频流的逐帧性别分类的不同投票对置信度方法的影响，以提高分类精度。研究了将浅层网络分组为集合的可能性；已经表明，通过对单个帧或不同帧的集合内单独的浅层 CNN 分类结果进行进一步投票，准确性可能会得到更大的提高。