Enhanced Fingerprint Detection and Rectification System

Abstract

Information security is the process of securing the information or data from unauthorized access. Fingerprint authentication is proven to be more secure because it authorizes the unique feature of on finger. Fingerprint recognition system has been suffering through Positive and Negative classifications. In positive classification, the physical access control systems and user should negotiate for self-identification. The false case classification broadly talks about low quality of images in case of user identification may authenticate malicious user. Distortion detection can be categorized in two classification problems, which can be solved using the registered ridge orientation map and period map of a fingerprint which is used as the feature vector and CNN is trained to perform the classification and rectification (or equivalently distortion field estimation) task which can be viewed as a regression problem, where the input is a distorted fingerprint and the output is a distortion field. For such problem, Detection and Rectification of the distorted fingerprint is must. Distortion rectification is used to transform a distorted fingerprint into a normal one so as to increase the recognition rate of existing fingerprint recognition algorithms for distorted fingerprints. CNN is an efficient distortion rectification method and the processing speed is significantly faster than other methods.