A survey on minutiae-based palmprint feature representations, and a full analysis of palmprint feature representation role in latent identification performance (original) (raw)
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Palmprint Recognition in Uncontrolled and Uncooperative Environment
IJRASET, 2021
On-line palmprint recognition and latent palmprint identification unit two branches of palmprint studies. The previous uses middle-resolution footage collected by a camera in an exceedingly} very well-controlled or contact-based surroundings with user cooperation for industrial applications and so the latter uses high resolution latent palmprints collected in crime scenes for rhetorical investigation. However, these two branches do not cowl some palmprint footage that have the potential for rhetorical investigation. Attributable to the prevalence of smartphone and shopper camera, further proof is at intervals the variability of digital footage taken in uncontrolled and uncooperative surroundings. However, their palms area unit typically noticeable. To visualize palmprint identification on footage collected in uncontrolled and uncooperative surroundings, a novel palmprint info is established Associate in nursing AN end-to-end deep learning rule is projected. The new data named NTU Palmprints from the net (NTU-PI-v1) contains 7881 footage from 2035 palms collected from the net. The projected rule consists of Associate in Nursing alignment network and a feature extraction network and is end-to-end trainable. The projected rule is compared with the progressive on-line palmprint recognition ways that and evaluated on three public contactless palmprint infos, IITD, CASIA, and PolyU and a couple of new databases, NTU-PI-v1 and NTU contactless palmprint info. The experimental results showed that the projected rule outperforms the current palmprint recognition ways that.
Subject Review: Feature Extraction Based on Palm Print
International Journal of Engineering Research and Advanced Technology (IJERAT), 2021
Physiological biometrics is one of the attractive fields for researchers due to its unique and stable features. One of these physical biometrics is "Palm Print". This new approach is used in personal recognition because of the powerful information that can be extracted from the palm print. The characteristics that are extracted keeping the rules of palmprint feature extraction are very important. In spite of the huge work done in this approach, the results recorded about the palm print are still uncompleted and the techniques that used in palm print feature extraction used in recognition are still continually modified. In this paper, we present a detailed background review for many techniques and methods that are used to extract features from palm print with many various methods and procedures. A comparison between these techniques is also presented.
Advanced Partial Palmprint Matching Based on Repeated Adjoining Minutiae
Citation/Export MLA Gayathri.R.Nayar, Aneesh R.P., “Advanced Partial Palmprint Matching Based on Repeated Adjoining Minutiae”, January 15 Volume 3 Issue 1 , International Journal on Recent and Innovation Trends in Computing and Communication (IJRITCC), ISSN: 2321-8169, PP: 378 - 381, DOI: 10.17762/ijritcc2321-8169.150175 APA Gayathri.R.Nayar, Aneesh R.P., January 15 Volume 3 Issue 1, “Advanced Partial Palmprint Matching Based on Repeated Adjoining Minutiae”, International Journal on Recent and Innovation Trends in Computing and Communication (IJRITCC), ISSN: 2321-8169, PP: 378 - 381, DOI: 10.17762/ijritcc2321-8169.150175
Enhanced Palm Print Images for Personal Accurate Identification
International Journal of Advance Research and Innovative Ideas in Education, 2018
In this paper, we propose an innovative touch-less palm print recognition system. This project is motivated by the public’s demand for non-invasive and hygienic bio metric technology. For various reasons, users are concerned about touching the bio metric scanners. Therefore, we propose to use a low-resolution web camera to capture the user’s hand at a distance for recognition. The users do not need to touch any device for their palm print to be extracted for analysis. A novel hand tracking and palm print region of interest (ROI) extraction technique are used to track and capture the user’s palm in real time video streams. The discriminated palm print features are extracted based on a new way that applies local binary pattern (LBP) texture descriptor on the palm print directional gradient responses. Experiments show promising result by using the proposed method. Performance can be further improved when a modified probabilistic neural network (PNN) is used for feature matching.
International Journal of Digital Crime and Forensics
The security of people requires a beefy guarantee in our society, particularly, with the spread of terrorism throughout the world. In this context, palmprint identification based on texture analysis is amongst the pattern recognition applications to recognize people. In this article, the researchers investigated a deep texture analysis for the palmprint texture pattern representation based on a fusion between several texture information extractions through multiple descriptors, such as HOG and Gabor Filters, Fractal dimensions and GLCM corresponding respectively to the frequency, model, and statistical methodologies-based texture features. They assessed the proposed deep texture analysis method as well as the applicability of the dimensionality reduction techniques and the correlation concept between the features-based fusion on the challenging PolyU, CASIA and IIT-Delhi Palmprint databases. The experimental results show that the fusion of different texture types using the correlati...
Feature Extraction Techniques for Palmprint Identification: A Survey
Abstract— Palmprint recognition has been investigated over the past decade. Palmprint recognition has five stages palmprint acquisition, pre-processing, feature extraction, enrolment (database) and matching. Due to rich information in palmprint it became a powerful means in person identification. The major approach for palmprint recognition is to extract feature vectors corresponding to individual palm image and to perform matching based on some distance metrics. Palmprint recognition is a challenging problem mainly due to low quality of pattern, large nonlinear distortion between different impression of same palm and large image size, which makes feature extraction and matching computationally demanding. In this paper we talk about the various approaches of palmprint recognition using matching pattern method.
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In these days Biometric based personnel authentication is evolving as robust method for security. A Palm print is one such reliable biometric entity showcasing all discriminating features. Reducing computational overhead is a challenge in palm print based biometric authentication system. In this paper we examined a new method for preliminary classification of palm print. An algorithm is proposed to implement proposed classification scheme. Experimentation and Test Results demonstrate classifying palm prints had been efficient using the proposed method.
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In this work, a new approach for personal authentication using palm image is presented. We design three ensembles of matchers which employ different feature representation schemes of the images: discrete cosine coefficients; invariant local binary patterns; Gabor filters. Each ensemble is obtained by varying the features used to train their matchers. Experimental results confirm that the three methods give complementary information which has been exploited by fusion rules. Finally, we combine our Palm based method system with other biometric characteristics that can be extracted from the hand (middle finger, ring finger, hand geometry), obtaining a further improvement of the performance.