Parts-Based Face Verification Using Local Frequency Bands

  • Christopher McCool
  • Sébastien Marcel
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5558)

Abstract

In this paper we extend the Parts-Based approach of face verification by performing a frequency-based decomposition. The Parts-Based approach divides the face into a set of blocks which are then considered to be separate observations, this is a spatial decomposition of the face. This paper extends the Parts-Based approach by also dividing the face in the frequency domain and treating each frequency response from an observation separately. This can be expressed as forming a set of sub-images where each sub-image represents the response to a different frequency of, for instance, the Discrete Cosine Transform. Each of these sub-images is treated separately by a Gaussian Mixture Model (GMM) based classifier. The classifiers from each sub-image are then combined using weighted summation with the weights being derived using linear logistic regression. It is shown on the BANCA database that this method improves the performance of the system from an Average Half Total Error Rate of 24.38% to 15.17% when compared to a GMM Parts-Based approach on Protocol P.

Keywords

Feature Vector Discrete Cosine Transform Gaussian Mixture Model Local Binary Pattern Feature Extraction Method 
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 2009

Authors and Affiliations

  • Christopher McCool
    • 1
  • Sébastien Marcel
    • 1
  1. 1.Centre du ParcIdiap Research InstituteMartignySwitzerland

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