A hierarchical approach to facial aging

Active Appearance Models (AAMs) have been used as a promising tool in the field of synthetic age progression. However, they are yet to be demonstrated on a large human population with wide variation. This paper presents a novel AAM-based hierarchical approach to facial aging. This work is motivated from studies in medical and anthropological literature on classification of human faces based on gender, ethnic and age groups. The proposed hierarchical model approach is a ethnicity and gender specific aging paradigm. Specifically, the Caucasian (European descent) and African American ethnic groups are considered. This work will further show that using individual hierarchical models generate better age-progressed synthetic images when compared to a general model approach. The results are evaluated by visual perception of the intended age group and preservation of identity. Also, a quantitative evaluation was performed using FaceVACS, a commercial face recognition system, as a surrogate measure. Higher match scores for synthetic images generated by hierarchical models when compared to those generated by a general model suggests the efficiency of the proposed hierarchical model approach.