Segmentation of Palm Vein Images Using U-Net

Biometric recognition methods using human traits like fingerprint, face, voice, palm-print, and palm vein have developed significantly in recent years. Palm vein recognition has gained attention because of its unique characteristics and high recognition accuracy. Many palm vein recognition methods proposed recently suffer from the issue of having low-quality images right at the acquisition stage, resulting in degradation of recognition accuracy. This paper proposes the use of a Convolutional Neural Network (CNN); U-Net, to effectively segment the vein networks from the background of near-infrared palm vein images. The experiments were conducted on the HK PolyU Multispectral Palmprint and Palmvein database. The original images taken from the database were reduced to region of interests. Morphological operations were applied to obtain ground truth mask images. The mask images were then used to train a modified U-Net in which Gabor filter was applied in the first block of the U-Net architecture. The accuracy of the segmented vein images was obtained by determining the overlap between the segmented images obtained from the network and the corresponding ground truth images from the morphological operations. The overlap is evaluated using the Jaccard Index and Dice Coefficient Metrics. For both of these similarity metrics, the value "0" indicates no overlap and "1" indicates a complete congruence between the subject images. The best Dice Coefficient obtained in this experiment is 0.69 and the Jaccard Index is 0.71, which makes this technique promising for automatic vein segmentation and can be adopted in palm vein recognition systems.