Biometric recognition of conjunctival vasculature using GLCM features

Besides the iris, conjunctival vasculature may also be used for ocular biometric recognition. Conjunctival vessel patterns can be easily observed in the visible spectrum and can compensate for off-angle or otherwise occluded iridial texture. In this paper, classification of conjunctival vasculature using Gray Level Co-occurrence Matrix (GLCM) is studied. Statistical features of GLCM, i.e., contrast, correlation, energy and homogeneity, were used in conjunction with Fisher linear discriminant analysis and regularized neural network classifiers in order to recognize textures arising from conjunctival vessels. Match score level fusion of Fisher LDA and neural networks provided the best results, resulting in a test set equal error rate (EER) and area under receiver operating characteristics curve (ROC AUC) of 13.97% and 0.9333, respectively. These figures improved to 11.9% and 0.9504 after fusion of LDA and neural network match scores.