IMPLEMENTATION OF NEURAL NETWORK BIDIRECTIONAL ASSOCIATIVE MEMORY FOR PALM HUMAN PATTERN RECOGNITION
ABSTRACT
Identity recognition system can be implemented by biometric system. Biometric system has better security grade than traditional identity recognition system because biometric system cannot be duplicated, stolen, lost, and forgotten. Palm is one of biometric system which pattern can be used to recognize identity. This Pattern can be identified by Artificial Neural Network (ANN). ANN is a computational system where the architecture and operation inspired by knowledge about biological neural cells in the brain. One method in artificial neural networks is Bidirectional Associative Memory (BAM) which is a type of neural network with hetero-associative memory by using two layers, that is the input layer and output layer which has the ability to identify pattern after reaching a stable state. In this research, the pattern used as the input pattern is the image of the palm of the right hand with the characteristic geometry and principle lines taken by scanner to produce image of transverse right palm, then processed to obtain a regular image with the size 200 x 300 pixels and converted to obtain bipolar patterns with values 1 and -1. This pattern will be used for training to produce a weight matrix which serve as a benchmark for testing the right palm pattern. This research has two tests, first testing on the right palm image pattern that was used for training with the result of identification is 100% and second testing on the image of right palm pattern not previously scanned with the identification result of 52,5%. The conclusion of the research the Bidirectional Associative Memory method is able to identify pattern by utilizing characteristic geometry and principle lines of the palm.
Keywords: Artificial Neural Network, Bidirectional Associative Memory, Palms pattern, Weight matrix, Bipolar patterns.
