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학술지 Uncooperative Person Recognition Based on Stochastic Information Updates and Environment Estimators
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김혜진, 김도형, 이재연, 정일권
ETRI Journal, v.37 no.2, pp.395-405
한국전자통신연구원 (ETRI)
We address the problem of uncooperative person recognition through continuous monitoring. Multiple modalities, such as face, height, clothes color, and voice, can be used when attempting to recognize a person. In general, not all modalities are available for a given frame; furthermore, only some modalities will be useful as some frames in a video sequence are of a quality that is too low to be able to recognize a person. We propose a method that makes use of stochastic information updates of temporal modalities and environment estimators to improve person recognition performance. The environment estimators provide information on whether a given modality is reliable enough to be used in a particular instance; such indicators mean that we can easily identify and eliminate meaningless data, thus increasing the overall efficiency of the method. Our proposed method was tested using movie clips acquired under an unconstrained environment that included a wide variation of scale and rotation; illumination changes; uncontrolled distances from a camera to users (varying from 0.5 m to 5 m); and natural views of the human body with various types of noise. In this real and challenging scenario, our proposed method resulted in an outstanding performance.
KSP 제안 키워드
Continuous monitoring, Human body, Illumination change, Person recognition, Unconstrained environment, multiple modalities, overall efficiency, recognition performance, video sequences