Fingerprint Gender Classification using Wavelet Transform and Singular Value Decomposition
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A novel method of gender Classification from fingerprint is proposed based on discrete wavelet transform (DWT) and singular value decomposition (SVD). The classification is achieved by extracting the energy computed from all the sub-bands of DWT combined with the spatial features of non-zero singular values obtained from the SVD of fingerprint images. K nearest neighbor (KNN) used as a classifier. This method is experimented with the internal database of 3570 fingerprints finger prints in which 1980 were male fingerprints and 1590 were female fingerprints. Finger-wise gender classification is achieved which is 94.32 left hand index finger of male persons. Gender classification for any finger of male persons tested is attained as 91.67 respectively. Overall classification rate is 88.28
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