상세정보
학술대회
Identity, Gender, and Age Recognition Convergence System for Robot Environments
- 발행일
- 201910
- 출처
- International Symposium on Robot and Human Interactive Communication (RO-MAN) 2019, pp.1-8
- 협약과제
- 19PS1800, 서비스 로봇의 사회적 상호작용을 위한 소셜 로봇지능 원천 기술 개발, 윤호섭
- 초록
- 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.
- KSP 제안 키워드
- Age recognition, Augmentation method, Fusion model, Gender Recognition, Identity Recognition, Learning data, Single-input, computation time, deep learning(DL), recognition performance, state-of-The-Art