Human Facial Age Estimation: Handcrafted Features Versus Deep Features

Abstract

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.