Automatic region segmentation for high-resolution palmprint recognition: Towards forensic scenarios

Abstract

Esta es la versión de autor del congreso publicado en: This is an author produced version of a paper published in: Abstract-Recently, a novel matching strategy based on regional fusion for high resolution palmprint recognition arises for both forensic and civil applications, under the concept of different regional discriminability of three palm regions, i.e., interdigital, hypothenar and thenar. This matching strategy requires accurate automatic region segmentation techniques since manual region segmentation is time consuming. In this work, we develop automatic region segmentation techniques based on datum point detection for high-resolution palmprint recognition which can be further applied to forensic applications. Firstly, Canny edge detector is applied to a full palmprint to obtain gradient magnitudes and strong edges. Then a first datum point, i.e., the endpoint of heart line, is detected by using convex hull on gradient magnitude image and its left/right differential image and strong edge image. A second datum point, i.e., the endpoint of life line, is estimated based on the position and direction of the first datum point and statistical average distance between the two datum points. Finally, segmented palm regions are generated based on the two datum points and their perpendicular bisector. To evaluate the accuracy of our region segmentation method, we compare the automatic segmentation with manual segmentation performed on a public high resolution palmprint database THUPALMLAB with full palmprint images. The regional error rates of interdigital, thenar and hypothenar regions are 15.72%, 17.05% and 21.38% respectively. And the total error rate is 19.54% relative to full palmprint images.

The Extract Region of Interest in High-Resolution Palmprint using 2D Image Histogram Entropy Function

“…However, this evaluation suffers from some limits, in particular when tests strike structured on conditions, different criteria and/or for the several database, making immediate comparisons harder. We will compare our work with an original work which was introduced by (Wang et al, 2013a) as shown in Table 3. Where (Wang et al, 2013a) proposed an algorithm to segment the palmprint image called automatic segmentation into three regions and used the last 50 subjects (50×2×8) from the THUPALMLAB database to evaluate their work.…”

“…We will compare our work with an original work which was introduced by (Wang et al, 2013a) as shown in Table 3. Where (Wang et al, 2013a) proposed an algorithm to segment the palmprint image called automatic segmentation into three regions and used the last 50 subjects (50×2×8) from the THUPALMLAB database to evaluate their work. They used the segmentation error parameter to measure the accuracy of the segmentation algorithm which is indicated in (Goumeidane and Khamadja, 2010).…”

“…They then applied the comparison score using logistic regression learning. Subsequently, Wang et al (2013a) introduced a method to automatically segment the region of the palmprint into three regions named as the interdigital, hypothenar and thenar using the datum point detection as illustrated by the following steps. First, apply a canny edge detector for all the palmprint images.…”

Multimodal Recognition System Based on High-Resolution Palmprints

“…In 2013, Wang et al [31] further developed an automatic region segmentation method, which could segment an image into three regions, namely, interdigital, thenar, and hypothenar regions, by applying a Canny edge detector to a full palmprint image, detecting the first data point by the convex hull, and estimating based on the position and direction of the first data point. Finally, the regions were segmented based on these points.…”

Regional fusion for high‐resolution palmprint recognition using spectral minutiae representation

“…The subset of THUPALMLAB includes the palmprint images from the last 50 subjects with 100 palms, that is, 800 ( = 50 × 2 × 8) images. Moreover, automatic segmentation based on datum points using the method proposed in [16] is also performed on the same subset. As reported in [16], there are 702 images segmented successfully in the subset of THUPALMLAB, among which there are 85 palms obtaining successful segmentation for their complete eight-image sets.…”