Improved Partial Fingerprint Matching Based on Score Level Fusion Using Pore and SIFT Features

Fingerprints are being extensively used for person identification in a number of commercial, civil, and forensic applications. Most of the current fingerprint verification systems utilize features that are based on minutiae points and ridge patterns. However, this technology suffers from the problem of handling incomplete prints and often discards any partial fingerprints obtained. The major challenges faced in partial fingerprint matching are the absence of sufficient level 2 features (minutiae) and other structures such as core and delta. In a multimodal biometric system, the effective fusion method is necessary for combining information from various single modality systems. In this paper we propose a method for partial fingerprint matching based on score level fusion by using pore and SIFT features. In this work involves extract the pore feature and SIFT feature points, then Matching algorithm is performed and matching score is calculated. Then score level fusion is applied. The experimental results are demonstrated on NIST SD30 database and achieves significantly better performance with low EER than either of the individual scheme. This novel method outperforms the traditional uni-matcher based scheme over a wide range of FAR and FRR values. The accuracy of proposed method is observed to be 97.32% for image size of 60%of original image partial with 1000ppi resolution fingerprint image and 97.15% for image size of 60%of original image partial with 500ppi resolution fingerprint image.