Automatic palmprint identification has received much attention in security applications and law enforcement. The performance of a palmprint identification system is improved by means of feature extraction and classification. Feature extraction methods such as Subspace learning are highly sensitive to the rotation variances, translation and illumination in image identification. Thus, Histogram of Oriented Lines (HOL) has not obtained promising performance for palmprint recognition so far. In this paper, we propose a new descriptor of palmprint named Improved Histogram of Oriented Lines (IHOL), which is an alternative of HOL. Improved HOL is not very sensitive to changes of translation and illumination, and has the robustness against small transformations whereas the small translation and rotations make no change in histogram value adjustment of the proposed work. The experiment results show that based on IHOL, with Principal Component Analysis (PCA) subspace learning can achieve high recognition rates. The proposed method (IHOL-Cosine distance) improves 1.30% on PolyU I database, and similarly (IHOL-Euclidean distance) improves 2.36% on COEP database compared with existing HOL method.

/pdf/palm-print-identification-using-improved-histogram-of-2lnnkfhb0v.pdf

Palm Print Identification Using Improved Histogram of Oriented Lines

The way that forensic examiners compare fingerprints highly differs from the behaviour of current automatic fingerprint identification algorithms. Experts usually use all the information in the fingerprint, not only minutiae, while automatic algorithms don’t. Partial (especially latent) fingerprint matching algorithms still report low accuracy values in comparison to those achieved by experts. This difference is mainly due to the features used in each case. In this work, a novel approach for matching partial fingerprints is presented. We introduce a new fingerprint feature, named Distinctive Ridge Point (DRP), combined with an improved triangle-based representation which also uses minutiae. The new feature describes the neighbouring ridges of minutiae in a novel way. A modified version of a fingerprint matching algorithm presented in a previous work is used for matching two triangular representations of minutiae and DRPs. The experiments conducted on NIST27 database with a background added of 29000 tenprint impressions from NIST14 and NIST4 databases showed the benefits of this approach. The results show that using the proposal we achieved an accuracy of 70.9% in rank-1, improving in an 11% the accuracy obtained using minutiae and the reference point. This result is comparable with the best accuracy reached in the state of the art while the amount of features is reduced.

Latent Fingerprint Matching Using Distinctive Ridge Points

Most of the previous research using palmprint as a biometric trait for personal authentication has concentrated on enhancing accuracy. In this paper, to speed up the recognition process, we propose a novel method using the statistical line based approach to improve the efficiency of the Palm Code. Normally, palm codes from different palm images are similar. The structural similarities between palmprints will reduce the performance of the palmprint identification system. Hence, to avoid the correlation between the palm codes, two elliptical Gabor filters with different orientations are used to extract the phase information, and two elliptical Gabor filters are used for the Fusion Code and the Orientation Code. After the Fusion Code and the Orientation Code have been obtained, they are fused to obtain a single feature vector, Palmprint Phase Orientation Code. The similarity between two palm images is measured, using the normalized hamming distance. Using the Hong Kong PolyU palmprint database our experimental results show that the proposed method gives a promising result.

/pdf/statistical-line-based-palmprint-recognition-using-2uovtg5o9f.pdf

Statistical line based palmprint recognition using elliptical gabor filters

This paper proposes an algorithm based on correlating a minutiae subset for matching partial fingerprints. From minutiae-sparse partial fingerprints, we first construct a super-template by combining the minutiae points from these partial fingerprints. Then, the subtemplates constructed through randomized selection will be matched against the super-template. In the experiment, we improve the recognition performance from 0.32 to 0.12 % in equal error rate after applying the minutiae subsets algorithm. Also, the experimental results show that the proposed method provides a good trade-off between speed and accuracy. Since the minutia-based fingerprint representation is an ANSI–NIST standard, our approach has the advantage of being directly applicable to existing databases. We present results of testing on FVC2004s DB1 database.

Partial Fingerprint Matching Using Minutiae Subset

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Statistical line based palmprint recognition using elliptical gabor filters

Partial Fingerprint Matching Using Minutiae Subset