PCA AND CENSUS TRANSFORM BASED FINGERPRINT RECOGNITION WITH HIGH ACCEPTANCE RATIO

Biometric techniques of authentication have been well known for the personal identification and have been in the great demand. Biometric method identifies persons by their physiological or behavioral characteristics such as fingerprint, face, retina, etc. Fingerprint recognition is one of the most used techniques among them. Fingerprint matching means matching of two fingerprints with fingerprint feature like ridge, minutia and other features of two fingerprint images. Fingerprint matching based on minutia pairings are use some time. But this technique is not very efficient for recognizing the low quality fingerprints. To overcome this problem, some researchers suggest the correlation technique which provides better result. Uses of correlation based methods are increasing today in the field of biometrics as it provides better results. The objective of this paper is to analyze the fingerprint verification techniques by extracting the features of fingerprints and enhance the fingerprint using image processing techniques to improve the matching percentage. The proposed method based on the PCA and census transform (CT) for fingerprint identification. In this method all the features of the fingerprint is enhance and used for the matching. CT is used for fingerprint feature extraction and PCA is used for fingerprint matching. For show the effectiveness also compared the results with the existing methods available in the literature and calculate the all the parameters like Acceptance Ratio (AR), Rejection Ratio (RR), False Matching Ratio (FMR), and False Non Matching Ratio (FMNR).