Dorsal Hand Vein Pattern Recognition Using Statistical Features and Artificial Neural Networks

Even though various dorsal hand vein pattern extraction techniques have been proposed for biometric identification, there remains considerable room for performance. This paper describes dorsal hand vein recognition using statistical and Gray Level Co-occurrence Matrix (GLCM) based features extraction techniques and artificial neural networks (ANN). For this purpose, 240 images of 80 users were obtained from Bosphorus Hand Vein Database. The images were first pre-processed by cropping region of interest (ROI), before the application of mean filtering, contrast enhancing and histogram equalizing. The ROI was then segmented by implementation of binarization method. The statistical and GLCM features were then extracted from the segmented ROI. These extracted features were sent to ANN for classification of the images. The training result shows that the proposed technique is able to recognize dorsal hand vein pattern with with considerably high accuracy of 99.32%.