Сверточные нейронные сети в задаче распознавания пола и возраста по видеоизображению
In this paper we examine the age and gender video-based recognition problem using deep convolutional neural networks. The comparative analysis of classifier fusion algorithms to aggregate decisions for individual frames is presented. In order to improve the age and gender identification accuracy we implement the video-based recognition system with several aggregation methods. We provide the experimental comparison for IJB-A, Indian Movies and Kinect datasets. It is demonstrated that the most accurate decisions are obtained using the geometric mean and mathematical expectation of the outputs at softmax layers of the convolutional neural networks for gender recognition and age prediction, respectively.