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Erschienen in:
2013 | OriginalPaper | Buchkapitel
Gender Recognition Using Nonsubsampled Contourlet Transform and WLD Descriptor
verfasst von : Muhammad Hussain, Sarah Al-Otaibi, Ghulam Muhammad, Hatim Aboalsamh, George Bebis, Anwar M. Mirza
Erschienen in: Image Analysis
Verlag: Springer Berlin Heidelberg
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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.