A prototype for palm print identity verification using hierarchical neural network architecture - Didulo, Dennis L. (Animo Repository, 1999)
A system for identifying a person using biometrics technology and artificial neural systems implemented using software simulation is developed in this study. The system extracts the features of the major creases in a person's palm from its digitized image and verifies the identity of the person against a list of enrolled identities. The software simulation has been implemented by the hierarchical neural network architecture consists of a self-organizing map (SOM) to select and extract features of the palm creases from the digitized image and an association layer as feature map classifier. The SOM makes use of competitive learning algorithm while the association layer uses backpropagation neural network (BPNN) architecture through supervised learning. A unique set of codes has been gathered out of the features of the palm creases of each individual. With the neural network's inherent capability for pattern recognition, the system was able to generate similar codes, if not exactly the same codes, for palm images belonging to the same individual. This became the basis for identity verification of the system.