Comparative Evaluation of Automatic Age-Progression Methodologies

Automatic age-progression is the process of modifying an image showing the face of a person in order to predict his/her future facial appearance. In this paper, we compare the performance of two age-progression methodologies reported in the literature against two novel approaches to the problem. In particular, we compare the performance of a method based on age prototypes, a method based on aging functions defined in a low-dimensional parametric model space, and two methods based on the distributions of samples belonging to different individuals and different age groups. Quantitative comparative results reported in the paper are based on dedicated performance evaluation metrics that assess the ability of each method to produce accurate predictions of the future/previous facial appearance of subjects. The framework proposed in this paper promotes the idea of a standardized performance evaluation protocol for age-progression methodologies, using images from a publicly available image database.

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