  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% for the left hand little fingers of female persons and 95.46% for the
left hand index finger of male persons. Gender classification for any finger of
male persons tested is attained as 91.67% and 84.69% for female persons
respectively. Overall classification rate is 88.28% has been achieved.
