Abstract
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.
Highlights
Recognition of face images is an important yet challenging problem
We present an analysis for the error introduced by the age group, gender, and race estimation of probe images compared to the actual age-groups, gender, and race in recognizing face images both for MORPH II and FERET
One can observe that aging features have the most significant impact on both the identification accuracy and mean average precision (mAP) compared to the race and gender features for MORPH II and FERET datasets
Summary
Recognition of face images is an important yet challenging problem. This challenge mainly includes pose, illumination, expression, and aging variations. A number of anthropometric studies such as [3] suggest significant facial morphological differences among race, gender, and age groups. We present demographic-assisted recognition and retrieval of face images across aging variations. The proposed approach involves: (i) facial-asymmetry-based demographic estimation and (ii) demographic-assisted face recognition and retrieval. To this end, we first estimated the age group, gender, and race of a query face image using facial-asymmetry-based, demographic-aware features learned by convolutional neural networks (CNNs). Does demographic-estimation-based re-ranking improve the face identification and retrieval performance across aging variations?.
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