A sparse representation denoising algorithm for finger-vein image based on dictionary learning

As an important method of biometric authentication, finger-vein recognition utilizes the unique finger-vein patterns to identify individuals at a high level of accuracy and safety. However, noise components, inherent in finger-vein images, pose a formidable challenge for extracting reliable finger-vein features for recognition. To tackle this challenge, intensive efforts have been directed at sparse representation (SR) methods, which can find the best representative of a test sample by a sparse linear combination of training samples (atoms) from a dictionary. Previous SR approaches treat training atoms equally for image representations, even if these atoms may vary in their effectiveness as feature descriptors, thus jeopardizing the denoising and recognition performances. To overcome this limitation, the present study proposed an adaptive SR with distance-based dictionary learning (DDL), enabling the ability to target more informative training samples. Specifically, based on the Euclidean distance, atoms in the dictionary are classified into two groups: the high-information and the low-information. Their weights for feature representations are assigned based on the distance entropy. Experimental results indicate that the developed SR-DDL denoising method, can suppress image noises and subsequently enhance the image recognition performance.