Expression Dynamics for Age Estimation

Estimation of a person's age from the facial image has many applications, ranging from biometrics and access control to cosmetics and entertainment. Many image-based methods have been proposed for this problem. In this work, we propose a method for the use of dynamic features in age estimation, and show that 1) the
temporal dynamics of facial features can be used to improve imagebased age estimation; 2) considered alone, static image-based features are more accurate than dynamic features. We have collected and annotated an extensive database of face videos from 400 subjects with an age range between 8 and 76, which allows us to extensively analyze the relevant aspects of the problem. The proposed system, which fuses facial appearance and expression dynamics, performs with a mean absolute error of 4.81 (±4.87) years. This represents a significant improvement of accuracy in comparison to the sole use of appearance-based features. Here you can download the dataset protocols, we have used in the following paper:

Dibeklioglu, H., T. Gevers, A.A. Salah, and R. Valenti, "A Smile Can Reveal Your Age: Enabling Facial Dynamics in Age Estimation," Proc. ACM International Conferance on Multimedia (ACM Multimedia), Nara, Japan, 2012.

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[ Experimental Protocols ]