Use of wavelet transforms and neural networks for identifying individuals through extracted features of the palm hand

Nowadays the biometry is one of the most reliable methods to identify a person. Therefore, identification methods as passwords have been substituted by biologics characteristics due the high level of security. So, in this paper we propose the use of the palm hand as a biometric characteristic and the use of the wavelet transform to extract the principal features of the palm. We also had used a pre-classification through principal lines to make the identification faster. The final identification was made by a neural network feedforward.