The proposed approach is evaluated using various face aging databases, i.e. The geometry constraints are also taken into account in the last step for more consistent age-progressed results. In addition, to enhance the wrinkles of faces in the later age ranges, the wrinkle models are further constructed using Restricted Boltzmann Machines to capture their variations in different facial regions. The Temporal Deep Restricted Boltzmann Machines based age progression model together with the prototype faces are then constructed to learn the aging transformation between faces in the sequence. In this approach, we first decompose the long-term age progress into a sequence of short-term changes and model it as a face sequence. This paper presents a deep model approach for face age progression that can efficiently capture the non-linear aging process and automatically synthesize a series of age-progressed faces in various age ranges. Abstract Modeling the face aging process is a challenging task due to large and non-linear variations present in different stages of face development.
