Finger-knuckle-print image enhancement based on brightness preserving dynamic fuzzy histogram equalization and filtering process

Finger-knuckle-print (FKP) is considered as one of the emerging hand biometric traits due to its potentiality toward the identification of individuals. However, extracting features out of poor contrast FKP images is the most challenging problem faced in this area. We propose a method for personal recognition using FKP images based on a preprocessing step to improve the contrast of input FKP image and a processing step for features extraction. In the first part, we compared the performances of different histogram equalization-based contrast enhancement algorithms. The enhanced image with better performance is considered in a second step for feature extraction and personal identification. We experimentally compared the proposed approach to other existing approaches in literature using PolyU FKP database framework, and results show that our technique performed favorably. (C) 2018 SPIE and IS&T.