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In various biometric applications, gender recognition from facial images plays an important role. In this paper, we investigate Weber's Local Descriptor (WLD) for gender recognition. WLD is a texture descriptor that performs better than other similar descriptors but it is holistic due to its very construction. We extend it by introducing local spatial information; divide an image into a number of blocks, calculate WLD descriptor for each block and concatenate them. This spatial WLD descriptor has better discriminatory power. Spatial WLD descriptor has three parameters. Through a large number of experiments performed on FERET database, we report the best combination of these parameters and that our proposed spatial WLD descriptor with simplest classifier gives much better accuracy i.e. 99.08% with lesser algorithmic complexity than state-of-the-art gender recognition approaches.
Date of Conference: 11-13 April 2012