Neural networks in video-based age and gender recognition on mobile platforms

Optical Memory and Neural Networks (Information Optics). 2018. Vol. 27. No. 4. P. 246–259.

A.S. Kharchevnikova, Savchenko A.

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-recording software. 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 sex recognition accuracy by 2-5% and 5-10% respectively.

Keywords: коллектив решающих правил, Dempster-Shafer theory, теория Демпстера-Шейфера, age and gender recognition, распознавание пола и возраста, convolution neural network, сверточная нейронная сеть, classifier fusion

Publication based on the results of: Разработка и апробация эффективных методов классификации для больших баз мультимедийных данных (2017)