LPIDB v1.0 - Latent palmprint identification database (original) (raw)

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.

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.

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.

On high resolution palmprint matching

2nd International Workshop on Biometrics and Forensics, 2014

Biometric systems are becoming a powerful tool for personal identification nowadays. They use people's physiological traits instead of something they possess or know, like tokens or passwords. One of the many possibilities for the use of biometric systems is in forensic environments, in which the system analyses biometric information taken from crime scenes in order to identify a culprit or a suspect. In this thesis, a high resolution palmprint biometric recognition system is proposed. The objective is to investigate the possibility, advantages and distinctive power of palmprints as a biometric trait for identification. The main obstacles to the proper identification of a subject through latent palmprints acquired in a scene come from the incompleteness of the prints, the arbitrary position and rotation between images and the degradation caused by Gaussian noise, salt & pepper noise and motion blur. Images corrupted by Gaussian noise are restored with an adaptive noise reduction filter, while images corrupted by salt & pepper noise are restored with a median filter. As for images disturbed by motion blur, the blur length is detected in the frequency domain and compensated using Wiener filtering. Experiments with undegraded full or partial to full palmprints matching revealed that if the true subject is enrolled in the database, the probability of getting a correct identification is greater than 90%. Better results could be achieved at the expense of more computation time. For degraded palmprints, the results vary considerably with the severity of the degradation.

Hierarchical palmprint identification via multiple feature extraction

Pattern Recognition, 2002

Biometric computing o ers an e ective approach to identify personal identity by using individual's unique, reliable and stable physical or behavioral characteristics. This paper describes a new method to authenticate individuals based on palmprint identiÿcation and veriÿcation. Firstly, a comparative study of palmprint feature extraction is presented. The concepts of texture feature and interesting points are introduced to deÿne palmprint features. A texture-based dynamic selection scheme is proposed to facilitate the fast search for the best matching of the sample in the database in a hierarchical fashion. The global texture energy, which is characterized with high convergence of inner-palm similarities and good dispersion of inter-palm discrimination, is used to guide the dynamic selection of a small set of similar candidates from the database at coarse level for further processing. An interesting point based image matching is performed on the selected similar patterns at ÿne level for the ÿnal conÿrmation. The experimental results demonstrate the e ectiveness and accuracy of the proposed method. ?

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.

A new method in locating and segmenting palmprint into region-of-interest

… Recognition, 2004

Various techniques in analyzing palmprint have been proposed but to the best of our knowledge, none has been studied on the selection and division of the region-ofinterest (ROI). Previous methods were always applied only to a fixed size square region chosen as the central part of the palm, which were then divided into square blocks for extraction of local features. In this paper, we proposed a new method in locating and segmenting the ROI for palmprint analysis, where the selected region varies with the size of the palm. Instead of square blocks, the region is divided into sectors of elliptical half-rings, which are less affected by misalignment due to rotational error. More importantly, our arrangement of the feature vectors ensures that only features extracted from the same spatial region of two aligned palms will be compared with each other. Encouraging results obtained favor the use of this method in the future development of palmprint analysis techniques.

Segment Based Hierarchical Palmprint Matching

Biometric Identification system has high efficiency, high recognition rate and comfortable to user’s operating characteristics. Palmprint are the most common authentic biometrics for personal identification, especially for forensic security. Palmprint authentication system is considered to be the most reliable biometric recognition due to its merits such as low-cost, user-friendliness, high speed and accuracy. In this paper, a novel hierarchical minutiae matching algorithm for palmprint identification system is proposed. Real time images are captured using a scanner. Each of these gray-scale images are aligned and then used to extract palmprint features. A hierarchical matching system that is used to reduce the computation cost by segmenting the image and matching it with the database, thereby false palmprints are rejected in the subsequent changes by comparing just a portion of the whole palmprint. The hierarchical strategy can reject many palmprint ( in the database of the AFIS ) which do not belong to the same hand as the input palmprint quickly, thus it can save much time.

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