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Mathematical Problems in Engineering
Volume 2015 (2015), Article ID 217568, 11 pages
Research Article

Partially Occluded Facial Image Retrieval Based on a Similarity Measurement

1SW·Content Research Laboratory, ETRI, Daejeon 305-700, Republic of Korea
2Department of Electronics Engineering, Kyungpook National University, Daegu 702-701, Republic of Korea

Received 22 September 2014; Revised 31 March 2015; Accepted 30 April 2015

Academic Editor: Aime’ Lay-Ekuakille

Copyright © 2015 Sohee Park et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


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.