One of the open come outs in fingerprint confirmation is the lack of robustness against image quality degradation. Poor-quality images result in specious and missing features, thus degrading the performance of the overall system. Therefore, it is very important for a fingerprint acknowledgement system to estimate the quality and validity of the captured fingerprint images. Also the elastic distortion of fingerprints is one of the major causes for false non-match. While this problem impacts all fingerprint acknowledged applications, it is especially unsafe in negative recognition applications, such as watch list and reduplication applications. In such applications, malicious users may purposely distort their fingerprints to elude identification. In the existing approaches by matching the different dataset by plotting Rigid Core Delta point to be specified as the finger print recognition. In this paper, we proposed novel algorithms to detect and rectify skin distortion based on a individual fingerprint image. In this process the detection is based on the patches based approaches. In these approaches the patches is defined as the rectangular position the distortion is not detected at the specified fingerprint. The patches size is varied the distortion is detection and a SVM classifier is prepared to perform the classification task. Distortion rectification (distortion field estimation) is considered as a regression problem, where the input used is a distorted fingerprint and the output is the distortion less field.
fingerprint, delta point, SVM, Matlab
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