Abstract

The paper considers the use of convolutional neural networks for the concurrent recognition of the gender and age of a person by video records of his face. The emphasis is on the incorporation of the approach into mobile video analytics systems. We have investigated the fusion of decisions obtained during the processing of each video frame, including the use of the classifier committee based on Dempster-Shafer theory. We propose the novel age prediction method using the evaluation of the expectation of the most probable ages. We have compared existing neural-net models with a specially trained modification of the MobileNet convolution network with two outputs. The experimental results are given for such data collections as Kinect, IJB-A, Indian Movie and EmotiW. As compared with other conventional methods, our approach makes it possible to increase the age and gender recognition accuracy by 2–5% and 5–10% respectively.

中文翻译：

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视频时代的神经网络和移动平台上的性别识别

摘要

本文考虑了使用卷积神经网络同时通过一个人的面部视频记录来识别一个人的性别和年龄。重点是将方法整合到移动视频分析系统中。我们研究了在处理每个视频帧期间获得的决策融合，包括使用基于Dempster-Shafer理论的分类器委员会。我们提出了一种新的年龄预测方法，即使用对最可能年龄的期望值进行评估。我们将现有的神经网络模型与经过特殊训练的带两个输出的MobileNet卷积网络进行了比较。给出了Kinect，IJB-A，Indian Movie和EmotiW等数据收集的实验结果。与其他常规方法相比，