Bimodal biometrics for financial infrastructure security

This research examines whether the integration of facial and fingerprint biometrics can improve the performance in financial infrastructure security such as ATM protection. Fingerprint biometrics consider distorted and misaligned fingerprints caused by environmental noise such as oil, wrinkles, dry skin, dirt and displacement of the query fingerprint with the database fingerprint template during matching. The noisy, distorted and/or misaligned fingerprint produced as a 2-D on x-y image, is enhanced and optimized using a new hybrid Modified Gabor Filter-Hierarchal Structure Check (MGF-HSC) system model based on an MGF integrated with an HSC. However, in order to improve the accuracy of financial infrastructure, face biometrics are introduced using a fast principal component analysis algorithm, in which different face conditions such as lighting, blurriness, pose, head orientation and other conditions are addressed. The MGF-HSC approach minimizes false fingerprint matching and the dominant effect of distortion and misalignment of fingerprints to an acceptable level. The proposed bimodal biometrics increase the accuracy of the False Rejection Rate (FRR) to 98% when the False Acceptance Rate (FAR) is 0.1% in an experiment conducted with 1000 test cases. This result shows that facial biometrics can be used to support fingerprint biometrics for improving financial security based on with significant improvement in both FRR and FAR.