A Contactless Fingerprint Verification Method using a Minutiae Matching Technique

Abstract

Most of matching or verification phases of fingerprint systems use minutiae types and orientation angle to find matched minutiae pairs from the input and template fingerprints. Unfortunately, due to some non-linear distortions, like excessive pressure and fingers twisting during enrollment, this process can cause the minutiae features to be distorted from the original. The authors are then interested in a fingerprint matching method using contactless images for fingerprint verification. After features extraction, they compute Euclidean distances between template minutiae (bifurcation and ending points) and input image minutiae. They compute then after bifurcation ridges orientation angles and ending point orientations. In the decision stage, they analyze the similarity between templates. The proposed algorithm has been tested on a set of 420 fingerprint images. The verification accuracy is found to be acceptable and the experimental results are promising.

Biometric System Vulnerabilities: A Typology of Metadata

“…Contactless fingerprint authentication [33] is a promising technique but one that is potentially vulnerable on several levels. For future work, we will explore the role of metadata in addressing contactless fingerprint vulnerabilities.…”

“…But increasingly, the biometric verification algorithms take into account these issues. [33] have proposed a contactless fingerprint recognition algorithm that addresses this issue. On another level, it should be noted that the false rejections can be due to noise introduced at the sensor such as, for example, the residual traces left on a fingerprint sensor.…”

Incorporating Metadata in Multibiometric Score-Level Fusion: an Optimized Architecture

“…It is based on the pixel technique and uses color spaces to characterize the skin (Abd El Hafeez, 2010, A New System for Extracting and Detecting Skin Color Regions from PDF Documents). For contactless fingerprint verification, the minutiae detection algorithm proposed by Djara et al [15] will be used.…”

“…[14],10]. Fingerprint matching techniques can be classified into three categories, namely minutiae-based matching, image-based matching and hybrid matching technique [15]. Minutiae-based matching essentially consists of finding the alignment between the pre-registered template and the input minutiae trait sets that result in the maximum number of minutiae pairings.…”

“…Traditional contact-based are also source of hygiene problems (spread of epidemics) and safety (risk of chemical attacks). For all these reasons, contactless 2D [15] and contactless 3D [17] fingerprint acquisition systems were introduced recently.…”