Video Stream Gender Classification Using Shallow CNN

Abstract

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.