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

  • Ivan HuertaEmail author
  • Carles Fernández
  • Andrea Prati
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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