Improving radial triangulation-based forensic palmprint recognition according to point pattern comparison by relaxation

Wang, Ruifang; Ramos, Daniel; Fiérrez, Julián

doi:10.1109/icb.2012.6199788

Cited by 8 publications

(5 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In our experiments, a dataset in which some other approaches were tested [6,9,10], was used. This dataset is conformed by 22 latent palmprints from real forensic cases and 8680 full palmprints from criminal investigation field, captured by Beijing Institute of Criminal Technology in China.…”

Section: Resultsmentioning

confidence: 99%

“…However, this proposal is time consuming and not robust to distortions. Another recent works use radial triangulations in order to extract features [9,10]. Even when the use of radial triangulations increase the accuracy, the features extracted from them are still affected by stretching in the skin.…”

Section: Introductionmentioning

confidence: 99%

“…One of the most relevant contribution of this paper is to present the results of our palmprint matching algorithm dealing with low quality or distorted palmprint images. It is important to note that unlike other approaches [6,7,9,10], our proposal do not uses any enhancement method in the minutiae extraction process. Our novel matching algorithm uses a representation proposed in literature [5], called expanded triangle set, which is based on minutia triplets obtained from Delaunay triangulation and other redundant ones for reducing the negative effect of structural distortions.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A New Triangular Matching Approach for Latent Palmprint Identification

Hernández-Palancar

Muñoz-Briseño

Gago-Alonso

2013

Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications

View full text Add to dashboard Cite

Palmprint identification is still considered as a challenging research line in Biometrics. Nowadays, the performance of this techniques highly depends on the quality of the involved palmprints, specially if the identification is performed in latent palmprints. In this paper, we propose a new feature model for representing palmprints and dealing with the problems of missing and spurious minutiae. Moreover, we propose a novel verification algorithm based in this feature model, which uses a strategy for finding adaptable local matches between substructures obtained from images. In experimentation, we show that our proposal achieves highest scores in latent palmprint matching, improving some of the best results reported in the literature.

show abstract

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A New Triangular Matching Approach for Latent Palmprint Identification

Hernández-Palancar

Muñoz-Briseño

Gago-Alonso

2013

Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications

View full text Add to dashboard Cite

show abstract

“…Wang et al introduced approaches that are based on high-resolution palmprints. In 2012, they [30] introduced a latent-to-full palmprint biometric system that is based on triangulation and logistic regression learning for score computation. This study was evaluated on a forensic database that included 22 latent palmprints from real cases and 8680 full palmprints from criminal investigations.…”

Section: Literature Reviewmentioning

confidence: 99%

Multimodal Recognition System Based on High-Resolution Palmprints

et al. 2020

View full text Add to dashboard Cite

High-resolution palmprint recognition is a challenging problem due to deficiencies in images, such as poor quality, skin distortion, and unallocated images. Considering the importance of high-resolution palmprints in forensic applications, this study proposes a novel multimodal palmprint scheme that combines the left and right palmprints using feature-level fusion by exploiting the similarity between the left and right palmprints using high-resolution images. The proposed system accepts as input palmprints that were captured at 500 ppi, which is the standard in forensic applications. The system is implemented by employing a statistical gray-level co-occurrence matrix (GLCM) as the texture feature extraction algorithm. Then, the features are ranked based on their probability distribution functions (PDFs) to select the most significant features. Finally, an enhanced probabilistic neural network (PNN) is used to estimate the recognition system. The benchmark THUPALMLAB database is used to conduct experiments, the results of which demonstrate that the proposed method can yield satisfactory results. INDEX TERMS GLCM, high-resolution palmprint, multimodal, PNN, ranking features.

show abstract

“…Since it deals with palmprints captured at 500 dpi at least, rich types of features, such as minutiae, principal lines, and even pores, can be used for high-resolution palmprint matching. Depending on the application fields, two kinds of matching strategy have been developed essentially following the minutiae-based matching methods, i.e., full-tofull palmprint comparison [1]- [3] for civil applications and partial-to-full/latent-to-full palmprint comparison [4]- [7] for forensic applications. As argued in the most recent work on forensic palmprint recognition [6], full-to-full palmprint matching algorithms would face problems when they are applied to forensic palmprint recognition where latent marks have much smaller area than full palmprints.…”

Section: Introductionmentioning

confidence: 99%

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

Wang

Ramos

Fiérrez

et al. 2013

2013 47th International Carnahan Conference on Security Technology (ICCST)

Self Cite

View full text Add to dashboard Cite

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.

show abstract

Improving radial triangulation-based forensic palmprint recognition according to point pattern comparison by relaxation

Abstract: Esta es la versión de autor de la comunicación de congreso publicada en: This is an author produced version of a paper published in:

Cited by 8 publications

References 16 publications

A New Triangular Matching Approach for Latent Palmprint Identification

A New Triangular Matching Approach for Latent Palmprint Identification

Multimodal Recognition System Based on High-Resolution Palmprints

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

Contact Info

Product

Resources

About