Abstract
Sweat pores on the human fingertip have meaningful patterns that enable individual identification. Although conventional automatic fingerprint identification systems (AFIS) have mainly employed the minutiae features to match fingerprints, there has been minimal research that uses sweat pores to match fingerprints. Recently, high-resolution optical sensors and pore-based fingerprint systems have become available, which motivates research on pore analysis. However, most existing pore-based AFIS methods use the minutia-ridge information and image pixel distribution, which limit their applications. In this context, this paper presents a stable pore matching algorithm which effectively removes both the minutia-ridge and fingerprint-device dependencies. Experimental results show that the proposed pore matching algorithm is more accurate for general fingerprint images and robust under noisy conditions compared with existing methods. The proposed method can be used to improve the performance of AFIS combined with the conventional minutiae-based methods. Since sweat pores can also be observed using various systems, removing of the fingerprint-device dependency will make the pore-based AFIS useful for wide applications including forensic science, which matches the latent fingerprint to the fingerprint image in databases.
Highlights
Since the human fingerprint is unique and not easy to imitate, fingerprint recognition has played an essential role in various applications, such as personal authentication, security investigation, and border control over the last few decades
By using this Levenberg-Marquardt algorithm (LMA) step, we can see that the pore matching accuracy is improved by 13.78%
We present a novel pore matching method using optimum geometric transformation and bipartite graph-based approach
Summary
Since the human fingerprint is unique and not easy to imitate, fingerprint recognition has played an essential role in various applications, such as personal authentication, security investigation, and border control over the last few decades. In automatic fingerprint identification systems (AFIS), image features for the fingerprint recognition are hierarchically categorized into three levels: Level 1. Because Level 1 features have little information, they are commonly helpful for the sketchy morphological categorization of fingerprints (e.g., arch, left loop, right loop, and double loop). Level 2 features cover Galton details and minutiae points (terminations and bifurcations of the friction ridge). Most studies in AFIS have focused on utilizing Level 2 features so far because they have enough patterns to recognize the human individuality [3]. Level 3 encompasses many fine details on the fingerprint that include ridge edge, ridge width, scars, and sweat pores. They carry an extensive amount of information. It is known that human latent fingerprint investigators often examine
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