Chapter

Image Analysis

Volume 7944 of the series Lecture Notes in Computer Science pp 373-383

Gender Recognition Using Nonsubsampled Contourlet Transform and WLD Descriptor

  • Muhammad HussainAffiliated withLancaster UniversityCollege of Computer and Information Sciences, King Saud University
  • , Sarah Al-OtaibiAffiliated withLancaster UniversityCollege of Computer and Information Sciences, King Saud University
  • , Ghulam MuhammadAffiliated withLancaster UniversityCollege of Computer and Information Sciences, King Saud University
  • , Hatim AboalsamhAffiliated withLancaster UniversityCollege of Computer and Information Sciences, King Saud University
  • , George BebisAffiliated withLancaster UniversityDepartment of Computer Science and Engineering, University of Nevada at Reno
  • , Anwar M. MirzaAffiliated withLancaster UniversityCollege of Computer and Information Sciences, King Saud University

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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