Vein pattern classification using convolutional neuronal network and moment invariants

Vein pattern recognition is a novel method to reliably identify or authenticate a person’s safety. It uses infrared images from the palm, wrist, or fingers, which shows the network of veins under the skin. This paper presents a Convolutional Neural Network (CNN) to classify infrared images of the hand vein pattern. The public PolyU Database is used to train the CNN. The CNN can classify 6000 vein patterns of the hand with an accuracy of 92.81%. Even more, its performance is compared with the invariant moment descriptors. In this case, vein pattern recognition is carried out on the raw images using k-Nearest Neighbors (k-NN) and invariant Zernike moments. An accuracy of 99.97% is obtained.