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Symmetry 2018, 10(5), 148;

Demographic-Assisted Age-Invariant Face Recognition and Retrieval

Department of Electrical Engineering, Mirpur University of Science and Technology, Mirpur 10250 (AJK), Pakistan
Faculty of Computing, Engineering and Science, Staffordshire University, Stoke-on-Trent ST4 2DE, UK
School of Electronic Information and Communications, Huazhong University of Science and Technology, Wuhan 430074, China
Author to whom correspondence should be addressed.
Received: 17 April 2018 / Revised: 2 May 2018 / Accepted: 4 May 2018 / Published: 8 May 2018
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Demographic estimation of human face images involves estimation of age group, gender, and race, which finds many applications, such as access control, forensics, and surveillance. Demographic estimation can help in designing such algorithms which lead to better understanding of the facial aging process and face recognition. Such a study has two parts—demographic estimation and subsequent face recognition and retrieval. In this paper, first we extract facial-asymmetry-based demographic informative features to estimate the age group, gender, and race of a given face image. The demographic features are then used to recognize and retrieve face images. Comparison of the demographic estimates from a state-of-the-art algorithm and the proposed approach is also presented. Experimental results on two longitudinal face datasets, the MORPH II and FERET, show that the proposed approach can compete the existing methods to recognize face images across aging variations. View Full-Text
Keywords: demographic estimation; facial asymmetry; face recognition; retrieval demographic estimation; facial asymmetry; face recognition; retrieval

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Sajid, M.; Shafique, T.; Manzoor, S.; Iqbal, F.; Talal, H.; Samad Qureshi, U.; Riaz, I. Demographic-Assisted Age-Invariant Face Recognition and Retrieval. Symmetry 2018, 10, 148.

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