Face Classification in Adults and Minors, an Approach based on Facial Anthropometry

Abstract

The process of age estimation is a task of computer vision that represents a challenge to the diverse factors that influence the process of aging in humans such as environment, food, and toxic habits, among others. Such a task has been studied from different approaches. In this work, a geometric approach is developed by using distances and proportions existing in every face. The functionality is increased by the use of fiducial points to locate any characteristic features of the face; its location is based on craniofacial growth studies used in the area of forensic anthropometry and plastic surgery. The location of 15 fiducial points is considered through the ASM algorithm and 8 distances between fiducial points, which statistically verify the difference in facial proportions between adults and minors. The distances obtained are analyzed with the Discriminant Analysis procedure to study the correlations between distances and the corresponding age group. From this, a model that allows classifying a new image in an age group is constructed: adults; those over 18 years of age, and minors; those under 17 years of age, obtaining 90% accuracy.