Finger-Vein Image Enhancement Based on Pulse Coupled Neural Network

As a promising biometric technique, the finger-vein image recognition has seen a recent surge of interest. However, the collected finger-vein images are often degraded seriously because of various vein patterns, uneven illumination, and unsatisfied sensor conditions. This makes vein representations unreliable and inevitably impairs recognition accuracy. In this paper, a new model based on the pulse coupled neural network (PCNN) is proposed to enhance finger-vein image quality, and further to improve the reliability of image recognition. First, a simplified PCNN model for finger-vein image enhancement is proposed to reduce computational complexity. Then, a new parameter setting scheme is developed to automatically adjust the parameters in the present PCNN model, without involving any empirical correlations or training. Finally, extensive experiments are carried out, and the performance of the proposed PCNN model is evaluated by calculating four metrics, including the gradient in the spatial domain (GSG), the gray contrast (GC), the information capacity (IC), and the deep evaluator based on the stacked auto encoder (DESAE). The results have verified the capability and reliability of the presented PCNN model as well as the parameter setting scheme in finger-vein image enhancement. In addition, the enhanced finger-vein images by the present PCNN model have been shown to be able to improve the recognition accuracy significantly.