Demographic-Assisted Age-Invariant Face Recognition and Retrieval

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 proc...

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Bibliographic Details
Published inSymmetry (Basel) Vol. 10; no. 5; p. 148
Main Authors Sajid, Muhammad, Shafique, Tamoor, Manzoor, Sohaib, Iqbal, Faisal, Talal, Hassan, Samad Qureshi, Usama, Riaz, Imran
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 01.05.2018
Subjects
Access control
Age
Algorithms
demographic estimation
Demographics
Face recognition
facial asymmetry
Facial recognition technology
Feature extraction
Feature recognition
Object recognition
Race
Retrieval
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Summary: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.
ISSN:2073-8994
2073-8994
DOI:10.3390/sym10050148