Improvements in Active Appearance Model based synthetic age progression for adult aging

Abstract

Normal adult aging in the face can drastically affect performance of face recognition systems. Synthetically generating age-progressed or age-regressed images for aiding recognizers is one method of improving the robustness of face-based biometrics. These synthetic age progressions may also aid human law enforcement and other applications. There has been wide interest in these techniques in recent years, and the use of Active Appearance Models (AAMs) for synthetic age progression has been shown to be a promising approach but has not yet been demonstrated on a large human population with wide variation. This paper presents improvements in AAM-based age progression that generate significantly improved visual results, taking into account a much wider gender, age, and ethnic range than published to date for age progression techniques.