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Age Regression from Soft Aligned Face Images Using Low Computational Resources

  • Juan Bekios-Calfa
  • José M. Buenaposada
  • Luis Baumela
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6669)

Abstract

The initial step in most facial age estimation systems consists of accurately aligning a model to the output of a face detector (e.g. an Active Appearance Model). This fitting process is very expensive in terms of computational resources and prone to get stuck in local minima. This makes it impractical for analysing faces in resource limited computing devices. In this paper we build a face age regressor that is able to work directly on faces cropped using a state-of-the-art face detector. Our procedure uses K nearest neighbours (K-NN) regression with a metric based on a properly tuned Fisher Linear Discriminant Analysis (LDA) projection matrix. On FG-NET we achieve a state-of-the-art Mean Absolute Error (MAE) of 5.72 years with manually aligned faces. Using face images cropped by a face detector we get a MAE of 6.87 years in the same database. Moreover, most of the algorithms presented in the literature have been evaluated on single database experiments and therefore, they report optimistically biased results. In our cross-database experiments we get a MAE of roughly 12 years, which would be the expected performance in a real world application.

Keywords

Linear Discriminant Analysis Discrete Cosine Transform Face Image Face Detector Active Appearance Model 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Juan Bekios-Calfa
    • 1
  • José M. Buenaposada
    • 2
  • Luis Baumela
    • 3
  1. 1.Dept. de Ingeniería de Sistemas y ComputaciónUniversidad Católica del NorteAntofagastaChile
  2. 2.Dept. de Ciencias de la ComputaciónUniversidad Rey Juan CarlosMóstolesSpain
  3. 3.Dept. de Inteligencia ArtificialUniversidad Politécnica de MadridBoadilla del MonteSpain

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