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학술지 Partially Occluded Facial Image Retrieval Based on a Similarity Measurement
Cited 19 time in scopus Download 24 time Share share facebook twitter linkedin kakaostory
저자
박소희, 이한성, 유장희, 김건우, 김순자
발행일
201504
출처
Mathematical Problems in Engineering, v.2015, pp.1-11
ISSN
1024-123X
출판사
Hindawi Publishing
DOI
https://dx.doi.org/10.1155/2015/217568
협약과제
13VS1100, 사람에 의한 안전위협의 실시간 인지를 위한 능동형 영상보안 서비스용 원거리 (CCTV 주간환경 5m이상) 사람 식별 및 검색 원천기술 개발, 유장희
초록
We present a partially occluded facial image retrieval method based on a similarity measurement for forensic applications. The main novelty of this method compared with other occluded face recognition algorithms is measuring the similarity based on Scale Invariant Feature Transform (SIFT) matching between normal gallery images and occluded probe images. The proposed method consists of four steps: (i) a Self-Quotient Image (SQI) is applied to input images, (ii) Gabor-Local Binary Pattern (Gabor-LBP) histogram features are extracted from the SQI images, (iii) the similarity between two compared images is measured by using the SIFT matching algorithm, and (iv) histogram intersection is performed on the SIFT-based similarity measurement. In experiments, we have successfully evaluated the performance of the proposed method with the commonly used benchmark database, including occluded facial images. The results show that the correct retrieval ratio was 94.07% in sunglasses occlusion and 93.33% in scarf occlusion. As such, the proposed method achieved better performance than other Gabor-LBP histogram-based face recognition algorithms in eyes-hidden occlusion of facial images.
KSP 제안 키워드
Facial image, Histogram feature, Histogram-based, Image retrieval, LBP histogram, Local binary Pattern, Occluded face recognition, Recognition algorithm, SIFT matching, Self-quotient image, Similarity Measurement