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Chapitre D'ouvrage Année : 2021

Human Facial Age Estimation: Handcrafted Features Versus Deep Features

Résumé

In recent times, human facial age estimation topic attracted a lot of attention due to its ability to improve biometrics systems. Recently, several applications that exploit demographic attributes have emerged. These applications include: access control, re-identification in surveillance videos, integrity of face images in social media, intelligent advertising, human–computer interaction, and law enforcement. In this chapter, we present a novel approach for human facial age estimation in facial images. The proposed approach consists of the following three main stages: (1) face preprocessing; (2) feature extraction (two different kinds of features are studied: handcrafted and deep features); (3) feeding the obtained features to a linear regressor. Also, we investigate the strength and weakness of handcrafted and deep features for facial age estimation. Experiments are conducted on three public databases (FG-NET, PAL and FACES). These experiments show that both handcrafted and deep features are effective for facial age estimation. © 2021, Springer Nature Switzerland AG.
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Dates et versions

hal-03541943 , version 1 (25-01-2022)

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S.E. Bekhouche, F. Dornaika, A. Ouafi, Abdelmalik Taleb-Ahmed. Human Facial Age Estimation: Handcrafted Features Versus Deep Features. Advances in Science, Technology and Innovation, Springer Nature, pp.31-37, 2021, ⟨10.1007/978-3-030-14647-4_3⟩. ⟨hal-03541943⟩
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