Facial Age Estimation Through the Fusion of Texture and Local Appearance Descriptors

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8926)


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.


Age estimation CCA HOG LBP SURF 


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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.DPDCEUniversity IUAVVeniceItaly
  2. 2.Herta SecurityBarcelonaSpain

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