Gender Recognition Using Nonsubsampled Contourlet Transform and WLD Descriptor

  • Muhammad Hussain
  • Sarah Al-Otaibi
  • Ghulam Muhammad
  • Hatim Aboalsamh
  • George Bebis
  • Anwar M. Mirza
Conference paper

DOI: 10.1007/978-3-642-38886-6_36

Part of the Lecture Notes in Computer Science book series (LNCS, volume 7944)
Cite this paper as:
Hussain M., Al-Otaibi S., Muhammad G., Aboalsamh H., Bebis G., Mirza A.M. (2013) Gender Recognition Using Nonsubsampled Contourlet Transform and WLD Descriptor. In: Kämäräinen JK., Koskela M. (eds) Image Analysis. SCIA 2013. Lecture Notes in Computer Science, vol 7944. Springer, Berlin, Heidelberg

Abstract

Gender recognition using facial images plays an important role in biometric technology. Multiscale texture descriptors perform better in gender recognition because they encode the multiscale facial microstructures in a better way. We present a gender recognition system that uses SVM, two-stage feature selection and multiscale texture feature based on Nonsubsampled Contourlet Transform and Weber law descriptor (NSCT-WLD). The proposed system has better recognition rate (99.50%) than the state-of-the-art methods on FERET database. This research also reveals that in NSCT decomposition what is essential for face recognition and what is important for other tasks like age detection.

Keywords

Gender recognition Face recognition WLD Descriptor Nonsubsampled Contourlet Transform Support Vector Machines 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Muhammad Hussain
    • 1
  • Sarah Al-Otaibi
    • 1
  • Ghulam Muhammad
    • 1
  • Hatim Aboalsamh
    • 1
  • George Bebis
    • 2
  • Anwar M. Mirza
    • 1
  1. 1.College of Computer and Information SciencesKing Saud UniversityRiyadhSaudi Arabia
  2. 2.Department of Computer Science and EngineeringUniversity of Nevada at RenoUSA

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