Rotation Invariant Fingerprint Matching based on Gray values using SLFNN - Kumar, Ravinder (International Journal of Computer Sciences and Engineering, 2025)
Fingerprint matching is most widely used mean of person identification or verification since last two decades. The issues related to efficient matching under transformation requires lots of attention of the research community. This paper presents rotational invariant directional features computed directly from gray values of fingerprint images and referred as Local Directional Pattern (LDP). Single hidden Layer Feed Forward Neural Network (SLFNN) is proposed to be used for classification. Network is trained using four different training algorithms to determine the suitability of these algorithms. The results show that these features are very discriminatory under rotation and also the efficiency of SLFNN for matching. It is also evident that Resilient Propagation (RP) algorithm is much faster and gives best performance as compared to other training algorithms