Recognizing Deformed Fingerprint Images Using SVM Classifier

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Arushi Aggarwal
Dr. Chander Kant


Biometric arrangements works on behavioral and physiological biometric data to recognize a person. The behavioral biometric parameters are signature, speech, gait and keystroke, these parameters may change with time period. Though physiological characteristics such as face, fingerprint, palm print and iris stays unchanged with the existence period of person. Automatic fingerprint recognition systems (AFRS), as well-known biometric methods, are nowadays extensively utilized in assorted requests such as forensics and admission control. Fingerprints have been utilized as a reliable biometric feature for confidential identification. All Fingerprint-authentication system are based on various matching techniques, that can be generally categorized as minutiae based and correlation based. In Pattern recognition various modalities being used are fingerprint, palm print, footprint. But these pattern recognition methods cannot identify the persons with deformed finger. The problem of deformed fingerprint is extremely disparate from that of fingerprint spoofing. The objective of this paper is to propose an algorithm for recognizing deformed fingerprint. The Proposed method SVM-classifier is effective searching technique over the huge clustered fingerprint database. This Paper also presents a Feature and SVM established fingerprint trooping of the methods transpiring in the scrutiny area for adjusted fingerprints.

Keywords: Finger Print Recognition, Automated fingerprint recognition systems (AFRSs), Pattern Recognition, Deformed fingerprint, Support vector machines(SVM).


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