상세정보
학술지
Robust Deep Age Estimation Method Using Artificially Generated Image Set
- 발행일
- 201710
- 출처
- ETRI Journal, v.39 no.5, pp.643-651
- ISSN
- 1225-6463
- 출판사
- 한국전자통신연구원 (ETRI)
- 협약과제
- 16IC1200, 실환경하에서 인지센서네트워크(PSN) 기반 지능형 로봇의 사용자 정보(신원, 행동, 위치) 자동 추출 및 인식 기술 개발, 윤호섭
- 초록
- Human age estimation is one of the key factors in the field of Human-Robot Interaction/Human-Computer Interaction (HRI/HCI). Owing to the development of deep-learning technologies, age recognition has recently been attempted. In general, however, deep learning techniques require a large-scale database, and for age learning with variations, a conventional database is insufficient. For this reason, we propose an age estimation method using artificially generated data. Image data are artificially generated through 3D information, thus solving the problem of shortage of training data, and helping with the training of the deep-learning technique. Augmentation using 3D has advantages over 2D because it creates new images with more information. We use a deep architecture as a pre-trained model, and improve the estimation capacity using artificially augmented training images. The deep architecture can outperform traditional estimation methods, and the improved method showed increased reliability. We have achieved state-of-the-art performance using the proposed method in the Morph- II dataset and have proven that the proposed method can be used effectively using the Adience dataset.
- KSP 제안 키워드
- 3D information, Age recognition, Art performance, Conventional Database, Deep architecture, Estimation method, Human age estimation, Human-Robot Interaction(HRI), Image data, Improved method, Key factor
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