Identity, Gender, and Age Recognition Convergence System for Robot Environments

Abstract

This paper proposes a new dentity, gender, and age recognition convergence system for robot environments. In a robot environment, it is difficult to apply deep learning based methods because of various limitations. To overcome the limitations, we propose a shallow deep-learning fusion model that can calculate identity, gender, and age at once, and a technique for improving recognition performance. Using convergence network, we can obtain three pieces of information from a single input through a single operation. In addition, we propose a 2D / 3D augmentation method to generate virtual additional datasets for learning data. The proposed method has a smaller model size and faster computation time than existing methods and uses a very small number of parameters. Through the proposed method, we finally achieved 99.35%, 90.0%, and 60.9% / 94.5% of performance in identity recognition, gender recognition, and age recognition. In all experiments, we did not exceed the state-of-the-art results, but compared to other studies, we obtained performance similar to the previous study using only less than 10% parameters. In some experiments, we also achieved state-of-the-art result.