FINGERPRINT QUALITY ESTIMATION AND MATCHING ALGORITHMS

Abstract

Accurate and reliable automatic personal identification is critical in wide range of application domains such as National ID card, Electronic Commerce, ATMs etc. Biometrics which refers to automatic identification of a person based on his physiological or behavioral characteristics is inherently more reliable, in differentiating an authorized person from an imposter, than traditional password and PIN number based methods. Among all the biometric techniques, fingerprint based authentication is mostly used because of its reliability, low cost and ease of integration. The performance of automatic fingerprint identification systems relies heavily on the quality of fingerprint images. The quality of fingerprint image effects the accurate extraction of minutiae. The low quality image contains large number of false minutiae as compared to good quality image. In this dissertation, a novel technique has been proposed for quality estimation of fingerprint images. A set of statistical and frequency features has been calculated from the fingerprint image. K-means clustering algorithm has been utilized to classify the fingerprint image into four classes i.e. good, dry, normal and wet. It has been shown through experimental results that the performance of minutiae based matcher is improved when the quality of fingerprint image is incorporated in the matching stage.