Age and Gender Estimation from Fingerprint Images

Age and gender have been extracted using the entropy, Ridge to Valley Area (RVA) and neural-based training of the energies of fingerprints using haar wavelet transform. Gender has been proposed based on fingerprint entropy. The age has been estimated using the energies of the fingerprint computed up to 3rd level of the haar wavelet. It is proposed to decompose the fingerprint at level 3 thereby computing the 12 numbers of energy levels. These energy levels along with RVA and entropy are made as input neurons to a back propagation neural network with two hidden neurons and four output classes. The back propagation neural network is trained using 300 samples of fingerprint images with 150 male and 150 female images. The weights are adjusted with a target MSE of 0.00001. The neural network is trained at a learning rate of 0.1, thereby giving fast learning of the network with weights adjusted to tune the four output classes i.e. 10-15, 16-20, 21-25, and 26-30 age groups.