Using a Triangular Matching Approach for Latent Fingerprint and Palmprint Identification

Abstract

The identification of fingerprints and palmprints is considered a challenging research line in Biometrics. Nowadays, the accuracy of these techniques highly depends on the quality of the involved impressions, specially if the matching is performed in latent cases. In the present work, a new algorithm that reports a high accuracy in both cases is presented. This proposal requires a minimum amount of manually marked information in latent impressions and deals successfully with problems of missing and spurious minutiae. Moreover, we improved a verification algorithm based on a previously introduced feature model. The algorithm uses a strategy for finding adaptable local matches between substructures obtained from images. The experimental results show that our proposal achieves high accuracy for all cases, despite the major differences that exist between a palmprint and a fingerprint. For latent fingerprint identification our approach shows its robustness in retrieving 258 latents from different size scale of the background dataset, achieving a rank-1 identification rate over 57% in all cases. Carrying out a similar experimentation for latent palmprint identification, our approach achieved a rank-1 identification rate over 75%.

Fuzzy Logic-Based Machine Learning Algorithm for Cultural and Creative Product Design

“…The number of populations is set by the designer according to the actual demand. The individual coding method adopted in the color scheme is where x is the number of color partitions; d i is the i -th color scheme of the product, i = 1, 2, ..., x ; y is the number of colors included in the product color scheme; r j , g j , b j is the R , G , B color value of a color scheme, respectively, taking values between 0 and 255 [ 28 , 29 ].…”

A Review of Fingerprint Feature Representations and Their Applications for Latent Fingerprint Identification: Trends and Evaluation

“…Additionally, the authors defined eight types of minutiae: termination, bifurcation, island, spur, crossover, bridge, and small crests. Nevertheless, some authors [25], [74]- [76] have claimed that the use of several minutia types is not suitable for representing latent fingerprints because they can easily be confused due to the difference of pressure of the finger over the surface and the background noise of the surface.…”

Latent Fingerprint Matching Using Distinctive Ridge Points

“…Let M q = S q , T q , F q and M t = S t , T t , F t be the query and template models, respectively. The matching algorithm proposed to be used is an enhanced version of the algorithm presented by Hernández-Palancar et al (2014). This algorithm is modified according to the intrinsic properties of the introduced representation.…”

“…The matching method used is a previous work (Hernández-Palancar et al, 2014) with the explained modifications added. The general idea of the algorithm is presented in Algorithm 3.…”

“…Figures 7(a) and 7(b) show the performance for the current proposal using tenprint edited minutiae and tenprint automatic extracted minutiae, respectively. In both figures, three CMC curves are plotted for representing the identification rate on three different scenarios: i) the matching algorithm presented by Hernández-Palancar et al (2014) using minutiae (Min) and reference point (RP), ii) the current proposal using minutiae (Min), reference point (RP), minutiae projections (Pr) and minutiae extensions (Em) and iii) the current proposal using minutiae (Min), reference point (RP), minutiae projections (Pr), minutiae extensions (Em) and extensions from projections (Ep).…”