A Competent Algorithm for Enhancing Low-Quality Finger Vein Images Using Fuzzy Theory

TLDR: For efficient enhancement of the finger vein images, interval type-2 fuzzy set is presented in this work and Einstein T-conorm is suggested for type reduction by combining the upper and lower membership functions.

Abstract: Soft computing methods and the fuzzy theoretic approaches, in particular, are widely known for their ability to tackle the uncertainties and vagueness that exist in image processing problems. This paper puts forward a distinctive enhancement algorithm for finger vein biometric images in which interval type-2 fuzzy sets are used. Finger vein biometrics is one of the latest reliable biometric systems that make use of the uniqueness of the finger vein patterns of individuals. Low contrast, blur, or noise often result in the lower quality of the captured finger vein images. For efficient enhancement of the finger vein images, interval type-2 fuzzy set is presented in this work and Einstein T-conorm is suggested for type reduction by combining the upper and lower membership functions. The performance assessment of the proposed algorithm is done by estimating the linear index of fuzziness and entropy. The experiments are performed using different vein pattern images, and the outcomes are analyzed by comparing with the existing methods. The performance evaluation visibly exhibits the efficiency of the recommended method in comparison with the existing methods.