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http://dx.doi.org/10.9717/kmms.2015.18.11.1268

Face Detection Using Multi-level Features for Privacy Protection in Large-scale Surveillance Video  

Lee, Seung Ho (School of Electrical Engineering, Korea Advanced Institute Science and Technology (KAIST))
Moon, Jung Ik (School of Electrical Engineering, Korea Advanced Institute Science and Technology (KAIST))
Kim, Hyung-Il (School of Electrical Engineering, Korea Advanced Institute Science and Technology (KAIST))
Ro, Yong Man (School of Electrical Engineering, Korea Advanced Institute Science and Technology (KAIST))
Publication Information
Journal of Korea Multimedia Society / v.18, no.11, 2015 , pp. 1268-1280 More about this Journal
Abstract
In video surveillance system, the exposure of a person's face is a serious threat to personal privacy. To protect the personal privacy in large amount of videos, an automatic face detection method is required to locate and mask the person's face. However, in real-world surveillance videos, the effectiveness of existing face detection methods could deteriorate due to large variations in facial appearance (e.g., facial pose, illumination etc.) or degraded face (e.g., occluded face, low-resolution face etc.). This paper proposes a new face detection method based on multi-level facial features. In a video frame, different kinds of spatial features are independently extracted, and analyzed, which could complement each other in the aforementioned challenges. Temporal domain analysis is also exploited to consolidate the proposed method. Experimental results show that, compared to competing methods, the proposed method is able to achieve very high recall rates while maintaining acceptable precision rates.
Keywords
Face Detection; Multi-level Facial Feature; Privacy Protection; Large-scale Video Surveillance System;
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