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학술대회 Identity, Gender, and Age Recognition Convergence System for Robot Environments
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저자
장재윤, 윤호섭, 김재홍
발행일
201910
출처
International Symposium on Robot and Human Interactive Communication (RO-MAN) 2019, pp.1-8
DOI
https://dx.doi.org/10.1109/RO-MAN46459.2019.8956313
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.
키워드
age recognition, convergence networks, face verification, gender classification.
KSP 제안 키워드
Age recognition, Augmentation method, Convergence networks, Fusion model, Gender Recognition, Identity Recognition, Learning data, Single-input, computation time, deep learning(DL), face verification