Orthogonal Bipolar Vectors as Multilayer Perceptron Targets for Biometric Pattern Recognition

This work proposes the unconventional use of orthogonal bipolar vectors (OBVs) as new targets for multilayer perceptron (MLP) training and test with biometric patterns represented by iris images. Nine different MLP models corresponding to nine different target vectors (including OBVs) have been developed for experimental performance comparison purposes. The experiments consisted of using biometric patterns from CASIA Iris Image Database developed by Chinese Academy of Sciences - Institute of Automation. The experimental results led to conclude that using OBVs as targets for MLP learning can provide better recognition performances rather than using other vectors as targets. Also, the results have shown that MLPs can be trained for OBVs spending smaller number of epochs to achieve relevant recognition rates compared to other types of target vectors. Therefore, the computational load for training MLPs can be reduced and biometric pattern recognition performances can be improved by using OBVs as targets.