Facial age estimation through the fusion of texture and local appearance descriptors

Abstract

Automatic extraction of soft biometric characteristics from face images is a very prolific field of research. Among these soft biometrics, age estimation can be very useful for several applications, such as advanced video surveillance [5,12], demographic statistics collection, business intelligence and customer profiling, and search optimization in large databases. However, estimating age from uncontrollable environments, with insufficient and incomplete training data, dealing with strong person-specificity, and high within-range variance, can be very challenging. These difficulties have been addressed in the past with complex and strongly hand-crafted descriptors, which make it difficult to replicate and compare the validity of posterior classification schemes. This paper presents a simple yet effective approach which fuses and exploits texture- and local appearance-based descriptors to achieve faster and more accurate results. A series of local descriptors and their combinations have been evaluated under a diversity of settings, and the extensive experiments carried out on two large databases (MORPH and FRGC) demonstrate state-of-the-art results over previous work.