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.

Facial age estimation through the fusion of Texture and Local Appearance descriptors / Ivan, Huerta; Carles, Fernández; Prati, Andrea. - ELETTRONICO. - (2015), pp. 667-681. ((Intervento presentato al convegno International Workshop on Soft Biometrics 2014 tenutosi a Zurich (Switzerland) nel September 7, 2014 [10.1007/978-3-319-16181-5_51].

Facial age estimation through the fusion of Texture and Local Appearance descriptors

PRATI, Andrea
Writing – Original Draft Preparation
2015

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.
978-331916180-8
Facial age estimation through the fusion of Texture and Local Appearance descriptors / Ivan, Huerta; Carles, Fernández; Prati, Andrea. - ELETTRONICO. - (2015), pp. 667-681. ((Intervento presentato al convegno International Workshop on Soft Biometrics 2014 tenutosi a Zurich (Switzerland) nel September 7, 2014 [10.1007/978-3-319-16181-5_51].
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11381/2809248
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