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학술대회 Human Age Estimation Using Multi-Class SVM
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저자
김계경, 강상승, 지수영, 김재홍
발행일
201510
출처
International Conference on Ubiquitous Robots and Ambient Intelligence (URAI) 2015, pp.370-372
DOI
https://dx.doi.org/10.1109/URAI.2015.7358911
협약과제
15MC1800, 다축 모션 플랫폼을 기반으로 한 범용 오감 융합형 스포츠 시뮬레이터 개발, 지수영
초록
Age estimation from face images has attracted attention because it is expected to have many application fields and growing interest. Human age estimation is very difficult tasks because a person has a different in appearance, which varies along with environment even same age. And also, pose, lighting condition or expression has an effect to estimate human age. Age estimation has challenged due to aforementioned problem even it has various potential application fields. In this paper, age estimation using Gabor feature and support vector machine as a classifier has proposed. Age-specific face images has saved in database, which has captured in real world environment. Age estimation result has applied to interact with sports simulator, which provides specialized information to each person, who wants to get individualized exercise model on sports simulator. We have evaluated age estimation performance on ETRI database, which has constructed during several months in real world environment.
키워드
Age estimation, Gabor feature, Human Sports Simulator Interaction, SVM classifier
KSP 제안 키워드
Application fields, Face Image, Gabor feature, Human Sports Simulator Interaction, Human age estimation, Lighting conditions, Multi-class SVM, Potential applications, Real-world, SVM Classifier, Support VectorMachine(SVM)