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Journal articles on the topic 'Palm vein pattern detection'

Author: Grafiati

Published: 4 June 2025

Last updated: 8 July 2025

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1

Mohd Noh, Zarina, Abdul Rahman Ramli, Marsyita Hanafi, M. Iqbal Saripan, and Ridza Azri Ramlee. "Palm Vein Pattern Visual Interpretation Using Laplacian and Frangi-Based Filter." Indonesian Journal of Electrical Engineering and Computer Science 10, no. 2 (2018): 578. http://dx.doi.org/10.11591/ijeecs.v10.i2.pp578-586.

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<span lang="EN-US">Detection of palm vein pattern through image processing techniques is an open problem as performance of each technique is closely related to the sample image gathered for the processing. The detected palm vein pattern is useful for further analysis in biometrics application and medical purpose. This paper aims to investigate the application of Laplacian filter and Frangi-based filter in detecting vein pattern contained in a near infrared illuminated palm image. Both filtering techniques are applied independently to two palm image databases to compare their performance in translating vein pattern in the image visually. Through empirical study, it is observed that Laplacian filter can translate the vein pattern in the image effectively. But pre-processings involved before the application of Laplacian filter need to be performed to accurately translate the vein pattern. The implementation of Frangi-based filter, while simplifying the detection process without the need of extra pre-processing, resulted in only certain vein pattern detected. Using pixel-by-pixel objective assessment, the rate for Laplacian filter in detecting vein pattern are generally more than 85% compared to Frangi-based filter; where it ranges from 60% to 100%.</span>

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Zarina, Mohd Noh, Rahman Ramli Abdul, Hanafi Marsyita, Iqbal Saripan M., and Azri Ramlee Ridza. "Palm Vein Pattern Visual Interpretation Using Laplacian and Frangi-Based Filter." Indonesian Journal of Electrical Engineering and Computer Science 10, no. 2 (2018): 578–86. https://doi.org/10.11591/ijeecs.v10.i2.pp578-586.

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Detection of palm vein pattern through image processing techniques is an open problem as performance of each technique is closely related to the sample image gathered for the processing. The detected palm vein pattern is useful for further analysis in biometrics application and medical purpose. This paper aims to investigate the application of Laplacian filter and Frangibased filter in detecting vein pattern contained in a near infrared illuminated palm image. Both filtering techniques are applied independently to two palm image databases to compare their performance in translating vein pattern in the image visually. Through empirical study, it is observed that Laplacian filter can translate the vein pattern in the image effectively. But preprocessings involved before the application of Laplacian filter need to be performed to accurately translate the vein pattern. The implementation of Frangi-based filter, while simplifying the detection process without the need of extra pre-processing, resulted in only certain vein pattern detected. Using pixel-by-pixel objective assessment, the rate for Laplacian filter in detecting vein pattern are generally more than 85% compared to Frangi-based filter; where it ranges from 60% to 100%.

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3

Muthupandian.V. "Palm Vein Pattern Authentication Systems." International Journal of Multidisciplinary Research Transactions 5, no. 6 (2023): 314–24. https://doi.org/10.5281/zenodo.7900061.

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Biometric may be a precocious theme of personal authentication victimization palm vein. The infrared palm image contains the data that is employed in our system; owing to vein data it provides lofty security in ATM. The project system includes: A palm vein image captured by the assistance of IR light-weight, detection in region interest palm vein extraction by multi-scale filtering and eventually matching their complete system is enforced on a DSP platform and equipped with a unique vein recognition formula. The project technology has several prospective applications like associate in Nursing radical secure system for ATMs and banking dealing, server log in system, associate in nursing authorization system for front doors, faculties hospitals wards cargo area, high security areas in airports, and even facilitating library disposition by doing away with the age recent card system. The experimental result that demonstrates the popularity victimization palm vein authentication is nice.

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Jain, Jagruti, Chitra Desai, and Mrunali Chavan. "PALM VEIN AUTHENTICATION TECHNOLOGY." International Journal of Engineering Technologies and Management Research 6, no. 12 (2020): 6–10. http://dx.doi.org/10.29121/ijetmr.v6.i12.2019.468.

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Palm vein authentication has high level of accuracy because it is located inside the body and does not change over the life and cannot be stolen. These papers present an analysis of palm vein pattern recognition algorithms, techniques, methodologies and system. It discusses the technical aspects of recent approaches for the following processes; detection of region of interest (ROI), segment of palm vein pattern, features extraction, and matching. The results show that, there is no benchmark database exists for palm vein recognition. For all processes, there are many machine learning techniques with very high accuracy.

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Miss., Jagruti Jain, Chitra Desai Miss., and Mrunali Chavan Miss. "PALM VEIN AUTHENTICATION TECHNOLOGY." International Journal of Engineering Technologies and Management Research 6, no. 12 (2019): 6–10. https://doi.org/10.5281/zenodo.3595234.

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Palm vein authentication has high level of accuracy because it is located inside the body and does not change over the life and cannot be stolen. These papers present an analysis of palm vein pattern recognition algorithms, techniques, methodologies and system. It discusses the technical aspects of recent approaches for the following processes; detection of region of interest (ROI), segment of palm vein pattern, features extraction, and matching. The results show that, there is no benchmark database exists for palm vein recognition. For all processes, there are many machine learning techniques with very high accuracy.

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6

Dai, Fen, Ziyang Wang, Xiangqun Zou, Rongwen Zhang, and Xiaoling Deng. "Noncontact Palm Vein ROI Extraction Based on Improved Lightweight HRnet in Complex Backgrounds." IET Biometrics 2024 (January 17, 2024): 1–15. http://dx.doi.org/10.1049/2024/4924184.

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The extraction of ROI (region of interest) was a key step in noncontact palm vein recognition, which was crucial for the subsequent feature extraction and feature matching. A noncontact palm vein ROI extraction algorithm based on the improved HRnet for keypoints localization was proposed for dealing with hand gesture irregularities, translation, scaling, and rotation in complex backgrounds. To reduce the computation time and model size for ultimate deploying in low-cost embedded systems, this improved HRnet was designed to be lightweight by reconstructing the residual block structure and adopting depth-separable convolution, which greatly reduced the model size and improved the inference speed of network forward propagation. Next, the palm vein ROI localization and palm vein recognition are processed in self-built dataset and two public datasets (CASIA and TJU-PV). The proposed improved HRnet algorithm achieved 97.36% accuracy for keypoints detection on self-built palm vein dataset and 98.23% and 98.74% accuracy for keypoints detection on two public palm vein datasets (CASIA and TJU-PV), respectively. The model size was only 0.45 M, and on a CPU with a clock speed of 3 GHz, the average running time of ROI extraction for one image was 0.029 s. Based on the keypoints and corresponding ROI extraction, the equal error rate (EER) of palm vein recognition was 0.000362%, 0.014541%, and 0.005951% and the false nonmatch rate was 0.000001%, 11.034725%, and 4.613714% (false match rate: 0.01%) in the self-built dataset, TJU-PV, and CASIA, respectively. The experimental result showed that the proposed algorithm was feasible and effective and provided a reliable experimental basis for the research of palm vein recognition technology.

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Iula, Antonio, and Alessia Vizzuso. "3D Vascular Pattern Extraction from Grayscale Volumetric Ultrasound Images for Biometric Recognition Purposes." Applied Sciences 12, no. 16 (2022): 8285. http://dx.doi.org/10.3390/app12168285.

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Recognition systems based on palm veins are gaining increasing attention as they are highly distinctive and very hard to counterfeit. Most popular systems are based on infrared radiation; they have the merit to be contactless but can provide only 2D patterns. Conversely, 3D patterns can be achieved with Doppler or photoacoustic methods, but these approaches require too long of an acquisition time. In this work, a method for extracting 3D vascular patterns from conventional grayscale volumetric images of the human hand, which can be collected in a short time, is proposed for the first time. It is based on the detection of low-brightness areas in B-mode images. Centroids of these areas in successive B-mode images are then linked through a minimum distance criterion. Preliminary verification and identification results, carried out on a database previously established for extracting 3D palmprint features, demonstrated good recognition performances: EER = 2%, ROC AUC = 99.92%, and an identification rate of 100%. As further merit, 3D vein pattern features can be fused to 3D palmprint features to implement a costless multimodal recognition system.

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Prasetio, Barlian Henryranu, Jevandika, and Dahnial Syauqy. "Rancang Bangun Alat Pengenal Finger Vein Menggunakan Raspberry Pi dengan Metode Convolutional Neural Network (CNN)." Jurnal Teknologi Informasi dan Ilmu Komputer 10, no. 7 (2023): 1409–16. http://dx.doi.org/10.25126/jtiik.1077950.

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Terobosan teknologi sistem pengenalan biometrik saat ini berkembang dengan pesat dan sangat mempermudah urusan seperti pengenalan identitas atau validasi identitas, serta tidak jarang perusahaan dan institusi lain yang umum menerapkan sistem pengenalan berbasis biometrik manusia seperti sidik jari, pola telapak tangan, wajah, ataupun iris mata. Sebuah sistem pengenalan biometrik memiliki kelebihan dan tentunya beberapa keterbatasan dalam hal performa dan kenyamanan. Pengenalan sidik jari dan telapak tangan mengharuskan pengguna untuk menyentuh permukaan sensor. Dengan cara ini, pengguna dapat merasa tidak nyaman dan risiko penyebaran virus atau bakteri sangat tinggi, juga akurasi pendeteksian dapat dipengaruhi oleh faktor-faktor seperti kulit berkeringat dan kering serta distorsi kulit. Oleh karena itu pada penelitian ini akan mengusung judul Rancang Bangun Alat Pengenal Finger Vein Menggunakan Raspberry Pi Dengan Metode Convolutional Neural Network (CNN). Penelitian ini menggunakan sistem berbasis Raspberry Pi 4 dengan bantuan IR LED dan webcam untuk proses akusisi data citra pembuluh darah jari, yang diharapkan mampu melakukan proses pengenalan Finger Vein lebih cepat, dan penggunaan metode Convolutional Neural Network yang sudah teruji untuk menghasilkan akurasi yang lebih baik dengan proses Deep Learning. Dari 30 data yang digunakan sebagai penguji sistem bersama perangkat lunak dan perangkat keras tertanam, akurasinya mencapai 96,66%. Abstract Breakthrough in biometric recognition system technology is currently growing rapidly and greatly facilitates matters such as identity recognition or identity validation, and it is not uncommon for companies and other institutions to implement human biometric-based recognition systems such as fingerprints, palm patterns, faces, or irises. A biometric recognition system has advantages and certainly some limitations in terms of performance and convenience. Fingerprint and palm recognition requires the user to touch the surface of the sensor. In this way, users can feel uncomfortable and the risk of spreading viruses or bacteria is very high, also the detection accuracy can be affected by factors such as sweaty and dry skin and skin distortion. Therefore, this study will carry the title Design a Finger Vein Recognition Tool Using Raspberry Pi with the Convolutional Neural Network (CNN) Method. This research uses a Raspberry Pi 4-based system with the help of IR LEDs and webcams for the acquisition process of finger blood vessel image data, which is expected to be able to carry out the Finger Vein recognition process faster, and the use of the proven Convolutional Neural Network method to produce better accuracy with the Deep Learning process. Of the 30 data used as system testers alongside embedded software and hardware, the accuracy reached 96.66%.

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Wu, Wei, Wei Qi Yuan, and Sen Lin. "An Instrument of Palm Vein Pattern Recognition." Applied Mechanics and Materials 333-335 (July 2013): 1092–95. http://dx.doi.org/10.4028/www.scientific.net/amm.333-335.1092.

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Palm vein pattern recognition is one of the newest biometric techniques researched today. This paper presents a palm vein recognition instrument that uses blood vessel patterns as a personal identifying factor. The instrument uses the recognition algorithm of two dimensional Fisher linear discriminant for classification. The experiment has been done in a self-build palm vein database. Experimental results show that the designed instrument achieves an acceptable level of performance.

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LI, QIANG, YAN'AN ZENG, and KUNTAO YANG. "WAVELET-BASED PALM VEIN RECOGNITION SYSTEM." Journal of Innovative Optical Health Sciences 03, no. 02 (2010): 131–34. http://dx.doi.org/10.1142/s1793545810000940.

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A new personal recognition system using the palm vein pattern is presented in this article. It is the first time that the palm vein pattern is used for personal recognition. The texture feature of palm vein is extracted by wavelet decomposition. With our palm vein image database, we employed the nearest neighbor (NN) classifier to test the performance of the system. Experimental results show that the algorithm based on wavelet transform can reach a correct recognition rate (CRR) of 98.8%.

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11

Baranov, S. O., and D. B. Abramov. "PALM VEIN PATTERN BIOMETRIC AUTHENTICATION TECHNOLOGY." Vestnik SibADI, no. 2(54) (January 1, 2017): 134–39. http://dx.doi.org/10.26518/2071-7296-2017-2(54)-134-139.

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12

Al-juboori, Ali Mohsin, Wei Bu, Xiangqian Wu, and Qiushi Zhao. "Palm Vein Verification Using Multiple Features and Locality Preserving Projections." Scientific World Journal 2014 (2014): 1–11. http://dx.doi.org/10.1155/2014/246083.

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Biometrics is defined as identifying people by their physiological characteristic, such as iris pattern, fingerprint, and face, or by some aspects of their behavior, such as voice, signature, and gesture. Considerable attention has been drawn on these issues during the last several decades. And many biometric systems for commercial applications have been successfully developed. Recently, the vein pattern biometric becomes increasingly attractive for its uniqueness, stability, and noninvasiveness. A vein pattern is the physical distribution structure of the blood vessels underneath a person’s skin. The palm vein pattern is very ganglion and it shows a huge number of vessels. The attitude of the palm vein vessels stays in the same location for the whole life and its pattern is definitely unique. In our work, the matching filter method is proposed for the palm vein image enhancement. New palm vein features extraction methods, global feature extracted based on wavelet coefficients and locality preserving projections (WLPP), and local feature based on local binary pattern variance and locality preserving projections (LBPV_LPP) have been proposed. Finally, the nearest neighbour matching method has been proposed that verified the test palm vein images. The experimental result shows that the EER to the proposed method is 0.1378%.

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13

Rameshbabu, A., J. Arunkumar, Mujibur Rahuman, and S. Hassan-Ul-Haq. "Palm Vein Authentication." Perspectives in Communication, Embedded-systems and Signal-processing - PiCES 4, no. 11 (2021): 273–76. https://doi.org/10.5281/zenodo.4592697.

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Nowadays Personal identification is one of the major problems in our society. The most common form of personal identification is usually involves a Personal Identification Number (PIN), password, and an ID card etc. Consequently there is a problem faced by people by loosing, stealing and sharing or even for getting the codes and cards. Biometric authentication has been widely accepted, safest and popular technique. The people prove their identity with unique biological characteristics such as fingerprint, iris, voice, face, gesture and hand geometry. Our approach is to design effective and secure contact less palm vein. Contact less design is more preferred as it offers more hygienic one. Our palm vein authentication system uses the vascular vein patterns as a personal identification factor which provides contact less and a very high level of accuracy. The proposed technology uses user’s palm vein pattern to ensure security and authentication of user information.

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Mohd Noh, Zarina, Abdul Rahman Ramli, Marsyita Hanafi, M. Iqbal Saripan, and Ridza Azri Ramlee. "Development of an Embedded Palm Vein Imaging Prototype." International Journal of Engineering & Technology 8, no. 1.1 (2019): 135–42. http://dx.doi.org/10.14419/ijet.v8i1.1.24792.

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This paper shares one of the available options in developing an embedded palm vein imaging prototype. The prototype was developed by the Raspberry Pi SBC to promote portability of the embedded system. With an integrated illumination circuit utilizing two near infrared (NIR) peak wavelengths of 850 nm and 870 nm, this paper explores the ability of the prototype to capture palm vein pattern information. The prototype program, and image analysis were executed by Python language environment and OpenCV module binding. The captured palm images were compared with palm image datasets from the Chinese Academy of Sciencesâ€™ Institute of Automation (CASIA) and the Hong Kong Polytechnic University (PolyU). The comparison was done in terms of observation of the image recorded and palm vein pattern revealed, and also through image assessment metrics. Results show that palm images captured by the prototype has the ability to record vein pattern information in the image with pixel-by-pixel similarity rate of 96.54% (median) for the extracted vein pattern, compared to the CASIA (median: 96.07%) and PolyU (median: 90.99%) datasets. As such, the developed prototype can be enhanced its usage not only for biometric acquisition, but also for medical purpose.Â Â

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Bhattacharyya, Debnath, Poulami Das, Tai-hoon Kim, and Samir Bandyopadhyay. "Vascular Pattern Analysis towards Pervasive Palm Vein Authentication." JUCS - Journal of Universal Computer Science 15, no. (5) (2009): 1081–89. https://doi.org/10.3217/jucs-015-05-1081.

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In this paper we propose an Image Analysis technique for Vascular Pattern of Hand Palm, which in turn leads towards Palm Vein Authentication of an individual. Near-Infrared Image of Palm Vein pattern is taken and passed through three different processes or algorithms to process the Infrared Image in such a way that the future authentication can be done accurately or almost exactly. These three different processes are: a. Vascular Pattern Marker Algorithm (VPMA); b. Vascular Pattern Extractor Algorithm (VPEA); and c. Vascular Pattern Thinning Algorithm (VPTA). The resultant Images will be stored in a Database, as the vascular patterns are unique to each individual, so future authentication can be done by comparing the pattern of veins in the palm of a person being authenticated with a pattern stored in a database.

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Wu, Wei. "Palm Vein Recognition Based on Independent Component Analysis." Applied Mechanics and Materials 333-335 (July 2013): 1106–9. http://dx.doi.org/10.4028/www.scientific.net/amm.333-335.1106.

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Palm vein pattern recognition is one of the newest biometric techniques researched today. This paper proposes project the palm vein image matrix based on independent component analysis directly, then calculates the Euclidean distance of the projection matrix, seeks the nearest distance for classification. The experiment has been done in a self-build palm vein database. Experimental results show that the algorithm of independent component analysis is suitable for palm vein recognition and the recognition performance is practical.

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Shah, Gunjan, Sagar Shirke, Sonam Sawant, and Yogesh H. Dandawate. "Palm vein pattern-based biometric recognition system." International Journal of Computer Applications in Technology 51, no. 2 (2015): 105. http://dx.doi.org/10.1504/ijcat.2015.068921.

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Wu, Wei, Wei Qi Yuan, and Hui Song. "A New Location Method of ROI for Contactless Palm Vein Recognition." Advanced Materials Research 760-762 (September 2013): 1398–401. http://dx.doi.org/10.4028/www.scientific.net/amr.760-762.1398.

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Palm vein pattern recognition is one of the newest biometric techniques researched today.At present, literatures selecte the center of the palm as the ROI of palm vein recognition. However the vein image in this area is not clear in some peoples palm. In this paper, we proposed a new location method of ROI which takes thenar area as the ROI. In the experiment part, it compares the recognition rate between the new and the traditional ROI in self-established contactless palm vein database. The result shows that this new method has got the recognition rate of 98.9258% and has increased recognition rate 2.0911% compared with the traditional one.

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Sari, Jayanti Yusmah, and Suharsono Bantun. "Contactless Biometric Based on Palm Vein Recognition Using Wavelet and Local Line Binary Patterns." Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) 7, no. 6 (2023): 1300–1308. http://dx.doi.org/10.29207/resti.v7i6.4530.

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To support the roadmap for coexistence with Covid-19, contactless biometrics is needed as an individual identity verification technology in daily activities such as control systems, recording attendance at offices/schools/agencies and access rights to a room. An example of contactless biometrics is palm vein based biometrics. Because it is contactless, this biometric system does not require direct contact between the user and the sensor device, thus providing several advantages in terms of comfort during acquisition and is more hygienic. In the palm vein recognition system, the palm vein pattern can be considered as a texture feature. Therefore, this study proposes a contactless biometric system based on palm vein recognition using the Local Line Binary Pattern method for extracting texture features of palm vein images resulting from the decomposition of 2D Wavelet Transformation, so as to produce a texture descriptor that is small and compatible with the texture characteristics of thin veins. The proposed texture feature extraction method has been tested using the Fuzzy k-NN classification method on 600 palm images with a CRR accuracy of 95.0% with a computation time of 0.057 seconds.

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Smorawa, Dorota, and Mariusz Kubanek. "Biometric Systems Based on Palm Vein Patterns." Journal of Telecommunications and Information Technology, no. 2 (June 30, 2015): 18–22. http://dx.doi.org/10.26636/jtit.2015.2.784.

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The work covers issues related to the design of biometric systems based on the hand vascular pattern. The study includes analysis of various stages of biometric systems design ranging from acquisition, feature extraction and biometric pattern creation for verification methods. The extraction methods based on two-dimensional density function and the extraction of the characteristic points – minutiae are presented. The article features the results of tests carried out on two different bases of blood vessels in a hand.

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Wu, Wei, Hui Song, and Ke Su. "Study of Palm Vein Imaging Based on NIR." Advanced Materials Research 760-762 (September 2013): 1406–9. http://dx.doi.org/10.4028/www.scientific.net/amr.760-762.1406.

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Palm vein pattern recognition is one of the newest biometric techniques researched today. In order to reduce the recognition calculation and increase the recognition accuracy, the region of interest (ROI) in the palm vein image must be selected. There are three areas on the palm: the center area of palm, the thenar area and the hypothenar area.This paper does contrast experiment of light absorption on the three area of palm. The experiment result is the thenar area is selected as the main scope of ROI.

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Lawal, Aderonke, Segun Aina, Samuel Okegbile, Seun Ayeni, Dare Omole, and Adeniran Ishola Oluwaranti. "Palm Vein Recognition System Based on Derived Pattern and Feature Vectors." International Journal of Digital Literacy and Digital Competence 8, no. 2 (2017): 56–72. http://dx.doi.org/10.4018/ijdldc.2017040104.

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Biometrics is a technology for recognition under which Palm vein recognition stems. They are of crucial importance in various applications of high sensitivity. This article develops a palm vein recognition model, based on derived pattern and feature vectors. All the palm print images used in this work were obtained from CASIA Multi-Spectral Palmprint Image Database V1.0 (CASIA database). First, a Region of Interest (ROI) was identified and extracted from the palm print images. Next, Histogram Equalization was used to enhance the area of the palm print image in the Region of Interest. The enhanced image obtained was subjected to the Zhang Suen's Thinning Algorithm to extract appropriate features in the palm print images needed for authentication. The features derived based on this vascular pattern thinning algorithm which are then compared and evaluated to carry out ‘matching'. The Pattern Matching itself was done using the Euclidean Distance for subsequent matching. The model was designed using UML, and implemented with C# and MS SQL on Microsoft Visual Studio platform. The developed system was evaluated based on False Acceptance, False Rejection and Equal Error Rate (EER) values obtained from the system. The results of testing and evaluation show that the developed system has achieved high recognition accuracy.

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Wu, Wei, Stephen John Elliott, Sen Lin, Shenshen Sun, and Yandong Tang. "Review of palm vein recognition." IET Biometrics 9, no. 1 (2019): 1–10. http://dx.doi.org/10.1049/iet-bmt.2019.0034.

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Verma, Dipti, and Sipi Dubey. "Fuzzy Brain Storm Optimization and Adaptive Thresholding for Multimodal Vein-Based Recognition System." International Journal of Pattern Recognition and Artificial Intelligence 31, no. 05 (2017): 1756007. http://dx.doi.org/10.1142/s0218001417560079.

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Nowadays, conventional security method of using passwords can be easily forged by unauthorized person. Hence, biometric cues such as fingerprints, voice, palm print, and face are more preferable for recognition but to preserve the liveliness, another one important biometric trait is vein pattern, which is formed by the subcutaneous blood vessels that contain all the achievable recognition properties. Accordingly, in this paper, we propose a multibiometric system using palm vein, hand vein, and finger vein. Here, Holoentropy-based thresholding mechanism is newly developed for extracting the vein patterns. Also, Fuzzy Brain Storm Optimization (FBSO) method is proposed for score level fusion to achieve the better recognition performance. These two contributions are effectively included in the biometric recognition system and the performance analysis of the proposed method is carried out using the benchmark datasets of palm vein image, finger vein image, and hand vein image. The quantitative results are analyzed with the help of FAR, FRR, and accuracy. From outcome, we proved that the proposed FBSO approach attained a higher accuracy of 81.3% than the existing methods.

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Hui Xiaowei, 惠晓威, 张俊宇 Zhang Junyu, 林森 Lin Sen, and 常正英 Chang Zhengying. "Application of Improved Local Directional Pattern in Palm Vein Recognition." Laser & Optoelectronics Progress 52, no. 7 (2015): 071001. http://dx.doi.org/10.3788/lop52.071001.

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Atikah Mohd Hayat, Nurul, Zarina Mohd Noh, Norhidayah Mohamad Yatim, and Syafeeza Ahmad Radzi. "Analysis of local binary pattern using uniform bins as palm vein pattern descriptor." Journal of Physics: Conference Series 1502 (March 2020): 012043. http://dx.doi.org/10.1088/1742-6596/1502/1/012043.

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Mirmohamadsadeghi, Leila, and Andrzej Drygajlo. "Palm vein recognition with local texture patterns." IET Biometrics 3, no. 4 (2014): 198–206. http://dx.doi.org/10.1049/iet-bmt.2013.0041.

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Kumar M, Ranjith, Deepika G. G, Meenakshi Krishnan, and Karthikeyan B. "An Open Source Contact-Free Palm Vein Recognition System." International Journal of Advances in Applied Sciences 6, no. 4 (2017): 319. http://dx.doi.org/10.11591/ijaas.v6.i4.pp319-324.

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In this document, we propose a novel palm vein<em> </em>recognition system using open source hardware and software. We have developed an alternative preprocessing and feature extraction technique. The proposed system is built on Raspberry Pi using OpenCV 2.4.12. The palm vein image is cropped to Region of Interest(ROI) to reduce the computational time in real time systems and then preprocessed to enhance the vein pattern visibility and to extract more number of key points using SIFT algorithm. Then the descriptors are stored in a dictionary like codebook file during training. Later the descriptors are tested with unknown patterns. The clustering is based on K-means algorithm and classification is done using Support Vector Machines (SVM).

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Raut, S. D., and V. T. Humbe. "An Approach to Boundary Extraction of Palm Lines and Vein Pattern." International Journal of Image, Graphics and Signal Processing 6, no. 12 (2014): 47–52. http://dx.doi.org/10.5815/ijigsp.2014.12.07.

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Glukhov, Oleg, Valentyn Anufriiev, and Valeriia Chekubasheva. "CREATION OF A SKIN MODEL FOR CALCULATING OPTICAL RADIATION PROPAGATION IN BIOMETRIC IDENTIFICATION SYSTEMS BASED ON THE VENOUS PATTERN OF THE PALM." Grail of Science, no. 43 (September 15, 2024): 281–84. http://dx.doi.org/10.36074/grail-of-science.06.09.2024.036.

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Introduction. Palm vein biometric identification systems are important for establishments that require accurate identification of individuals. These systems differ in their operation and structure, and developing and setting up a prototype to test the visibility of the venous pattern is time-consuming and resource-intensive, so it is advisable to conduct computer simulations of the passage of infrared radiation through the skin of the palm.

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Wijaya, Muhammad Adi, Priska Choirina, and Vincensius Arga Yoda. "Telekomunikasi Militer IMPLEMENTASI PALM VEIN SENSOR DENGAN METODE BIOMETRIC PATTERN PADA SISTEM SENTRY GUN BERBASIS FITUR LOCAL BINARY PATTERN (LBP)." Jurnal Telkommil 3, Mei (2022): 30–40. http://dx.doi.org/10.54317/kom.v3imei.229.

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Sentry Gun merupakan salah satu senjata api yang dapat digerakkan secara tidak langsung oleh manusia. Penggunaan alat ini untuk menghindari korban jiwa dari prajurit yang sedang berada di garis terdepan. Kelemahan sentry gun, adalah dapat digerakkan tidak langsung oleh manusia. Agar sistem tidak digunakan sembarangan oleh orang yang tidak berwenang, maka perlu proses autentifikasi. Proeses autentifikasi pada penelitian ini menggunakan biometrik palm vein sensor. Metode penelitian ini menggunakan metode Local Binary Pattern (LBP) yaitu dimana menggunakan metode eksperimen untuk mendapatkan data kuantitatif untuk membuktikan hipotesis. Hasil pengujian yang dilakukan didapatkan hasil jarak minimum pendeteksian adalah 0 cm dari jarak maksimum yang dideteksi yaitu 8 cm. Pengujian ini dipengaruhi oleh kualitas pixel dari kamera serta intensitas cahaya yang dilakukan pada pagi, siang dan malam hari, serta keadaan tangan yang terkena debu, lumpur ataupun kotoran yang mampu menutupi pola pembuluh vena yang ada pada punggung tangan kanan prajurit. Hasil penelitian menyatakan bahwa sistem tersebut mampu untuk memberikan keamanan dalam menggunakan akses tertentu bagi penggunannya dalam penggunaan senjata api ini, prosentase keberhasilan dari penggunaan palm vein sensor untuk autentifikasi pada sistem sentry gun ini adalah 85% sedangkan tingkat erornya adalah 15% ini dikarenakan pada letak dan kecepatan transfer data yang di ambil dari sentry gun dengan pengendali tembakan.

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Kang, Wenxiong, and Qiuxia Wu. "Contactless Palm Vein Recognition Using a Mutual Foreground-Based Local Binary Pattern." IEEE Transactions on Information Forensics and Security 9, no. 11 (2014): 1974–85. http://dx.doi.org/10.1109/tifs.2014.2361020.

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Gupta, Puneet, and Phalguni Gupta. "Multi-modal fusion of palm-dorsa vein pattern for accurate personal authentication." Knowledge-Based Systems 81 (June 2015): 117–30. http://dx.doi.org/10.1016/j.knosys.2015.03.007.

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Manoliu, Mitica-Valentin. "Biometric security: Recognition according to the pattern of palm veins." Scientific Bulletin of Naval Academy XXIII, no. 1 (2020): 257–62. http://dx.doi.org/10.21279/1454-864x-20-i1-036.

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Palm vein recognition is a promising new biometric method, which has additional potential in the forensic field. This process is performed using light using NIR(Near-infrared) LEDs and the camera that captures the acquisition of veins. The obtained images have noise with variations of rotation and translation. Therefore, the input image made by the camera must be pre-processed using characteristic processes. A set of features is extracted based on images taken from infrared light cameras and processed in order to make authentication possible. This whole process can be accomplished by several methods. Thus, the application can be used to improve the security of military ships in restricted areas, but not only.

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Lalithamani, N., and M. Sabrigiriraj. "Technique to generate face and palm vein-based fuzzy vault for multi-biometric cryptosystem." Machine Graphics and Vision 23, no. 1/2 (2012): 97–114. http://dx.doi.org/10.22630/mgv.2014.23.1.6.

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Template security of biometric systems is a vital issue and needs critical focus. The importance lies in the fact that unlike passwords, stolen biometric templates cannot be revoked. Hence, the biometric templates cannot be stored in plain format and needs strong protection against any forgery. In this paper, we present a technique to generate face and palm vein-based fuzzy vault for multi-biometric cryptosystem. Here, initially the input images are pre-processed using various processes to make images fit for further processing. In our proposed method, the features are extracted from the processed face and palm vein images by finding out unique common points. The chaff points are added to the already extracted points to obtain the combined feature vector. The secret key points which are generated based on the user key input (by using proposed method) are added to the combined feature vector to have the fuzzy vault. For decoding, the multi-modal biometric template from palm vein and face image is constructed and is combined with the stored fuzzy vault to generate the final key. Finally, the experimentation is conducted using the palm vein and face database available in the CASIA and JAFFE database. The evaluation metrics employed are FMR (False Match Ratio) and GMR (Genuine Match Ratio). From the metric values obtained for the proposed system, we can infer that the system has performed well.

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Bodade, Rajesh, and Saurabh Dixit. "Vein Pattern Decomposition (VPD) Method: a Novel Approach to Build Practical Palm Vein Based Person Authentication System." International Journal on Communications Antenna and Propagation (IRECAP) 7, no. 6 (2017): 516. http://dx.doi.org/10.15866/irecap.v7i6.13610.

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Ananthi, G., J. Raja Sekar, and S. Arivazhagan. "Ensembling Scale Invariant and Multiresolution Gabor Scores for Palm Vein Identification." Information Technology and Control 51, no. 4 (2022): 704–22. http://dx.doi.org/10.5755/j01.itc.51.4.30858.

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Biometric recognition based on palm vein trait has the advantages of liveness detection and high level of security. An improved human palm vein identification system based on ensembling the scores computed from scale invariant features and multiresolution adaptive Gabor features is proposed. In the training phase, from the input palm vein images, the interested palm regions are segmented using 3-valley point maximal palm extraction strategy, an improved method that extracts the maximal region of interest (ROI) easily and properly. Extracted ROI is enhanced using contrast limited adaptive histogram equalization method. From the enhanced image, local invariant features are extracted by applying scale invariant feature transform (SIFT). The texture and multiresolution features are extracted by employing adaptive Gabor filter over the enhanced image. These two features, scale invariant and multiresolution Gabor features act as the templates. In the testing phase, for the test images, ROI extraction, image enhancement, and two different feature extractions are performed. Using cosine similarity and match count-based classification, the score, Ss is computed for the SIFT features. Another score, Sg is computed using the normalized Hamming distance measure for the Gabor features. Both these scores are ensembled using the weighted sum rule to produce the final score, SF for identifying the person. Experiments conducted with CASIA multispectral palmprint image database version 1.0 and VERA palm vein database show that, the proposed method achieves equal error rate of 0.026% and 0.0205% respectively. For these databases, recognition rate of 99.73% and 99.89% respectively are obtained which is superior to the state-of-the-art methods in authentication and identification. The proposed work is suitable for applications wherein the authenticated person should not be considered as imposter.

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Piciucco, Emanuela, Emanuele Maiorana, and Patrizio Campisi. "Palm vein recognition using a high dynamic range approach." IET Biometrics 7, no. 5 (2018): 439–46. http://dx.doi.org/10.1049/iet-bmt.2017.0192.

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Mohammed, Hussein, Volker Märgner, and Giovanni Ciotti. "Learning-free pattern detection for manuscript research:." International Journal on Document Analysis and Recognition (IJDAR) 24, no. 3 (2021): 167–79. http://dx.doi.org/10.1007/s10032-021-00371-7.

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AbstractAutomatic pattern detection has become increasingly important for scholars in the humanities as the number of manuscripts that have been digitised has grown. Most of the state-of-the-art methods used for pattern detection depend on the availability of a large number of training samples, which are typically not available in the humanities as they involve tedious manual annotation by researchers (e.g. marking the location and size of words, drawings, seals and so on). This makes the applicability of such methods very limited within the field of manuscript research. We propose a learning-free approach based on a state-of-the-art Naïve Bayes Nearest-Neighbour classifier for the task of pattern detection in manuscript images. The method has already been successfully applied to an actual research question from South Asian studies about palm-leaf manuscripts. Furthermore, state-of-the-art results have been achieved on two extremely challenging datasets, namely the AMADI_LontarSet dataset of handwriting on palm leaves for word-spotting and the DocExplore dataset of medieval manuscripts for pattern detection. A performance analysis is provided as well in order to facilitate later comparisons by other researchers. Finally, an easy-to-use implementation of the proposed method is developed as a software tool and made freely available.

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Anufriiev, V. V., O. O. Levchenko, Ye V. Levchenko, V. A. Chekubasheva, O. V. Glukhov, and O. B. Galat. "Perspective of Creating Low-Cost Medical Assistant Robot Based on Waffle PI4 Platform with Palm Vein Pattern Scanner." Visnyk NTUU KPI Seriia - Radiotekhnika Radioaparatobuduvannia, no. 98 (December 30, 2024): 46–54. https://doi.org/10.20535/radap.2024.98.46-54.

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The purpose of this study is to develop a set of modifications for the TurtleBot 3 Waffle Pi robotic platform. One of the key achievements of this work is the creation of a biometric identification system based on the venous pattern of the palm. The principle of operation of the identification system is based on the use of infrared radiation absorbed by hemoglobin in the venous system of the palm. The absorbed radiation creates a clear pattern that can be captured using a camera without an infrared filter. The resulting image is pre-processed to reduce noise and unify with other images for further use in training a convolutional neural network used for patient identification. This identification method allows for high-speed and accurate patient identification, even with dirt or scratches on the palm. The described modifications are aimed at expanding the capabilities of the platform for military medical applications. By integrating these modifications into the TurtleBot 3 Waffle Pi robotic platform, military and civilian hospitals can improve their ability to provide timely and accurate medical care to those in need.

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Zabala-Blanco, David, Ruber Hernández-García, and Ricardo J. Barrientos. "SoftVein-WELM: A Weighted Extreme Learning Machine Model for Soft Biometrics on Palm Vein Images." Electronics 12, no. 17 (2023): 3608. http://dx.doi.org/10.3390/electronics12173608.

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Contactless biometric technologies such as palm vein recognition have gained more relevance in the present and immediate future due to the COVID-19 pandemic. Since certain soft biometrics like gender and age can generate variations in the visualization of palm vein patterns, these soft traits can reduce the penetration rate on large-scale databases for mass individual recognition. Due to the limited availability of public databases, few works report on the existing approaches to gender and age classification through vein pattern images. Moreover, soft biometric classification commonly faces the problem of imbalanced data class distributions, representing a limitation of the reported approaches. This paper introduces weighted extreme learning machine (W-ELM) models for gender and age classification based on palm vein images to address imbalanced data problems, improving the classification performance. The highlights of our proposal are that it avoids using a feature extraction process and can incorporate a weight matrix in optimizing the ELM model by exploiting the imbalanced nature of the data, which guarantees its application in realistic scenarios. In addition, we evaluate a new class distribution for soft biometrics on the VERA dataset and a new multi-label scheme identifying gender and age simultaneously. The experimental results demonstrate that both evaluated W-ELM models outperform previous existing approaches and a novel CNN-based method in terms of the accuracy and G-mean metrics, achieving accuracies of 98.91% and 99.53% for gender classification on VERA and PolyU, respectively. In more challenging scenarios for age and gender–age classifications on the VERA dataset, the proposed method reaches accuracies of 97.05% and 96.91%, respectively. The multi-label classification results suggest that further studies can be conducted on multi-task ELM for palm vein recognition.

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Ma, Xin, Xiaojun Jing, Hai Huang, Yuanhao Cui, and Junsheng Mu. "Palm vein recognition scheme based on an adaptive Gabor filter." IET Biometrics 6, no. 5 (2017): 325–33. http://dx.doi.org/10.1049/iet-bmt.2016.0085.

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Cheniti, Mohamed, Djamel Herbadji, Noubeil Guermat, Abderrahmane Herbadji, and Lahcene Ziet. "Personal authentication based on wrist and palm vein images." International Journal of Biometrics 11, no. 4 (2019): 309. http://dx.doi.org/10.1504/ijbm.2019.10023699.

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Herbadji, Abderrahmane, Noubeil Guermat, Lahcene Ziet, Mohamed Cheniti, and Djamel Herbadji. "Personal authentication based on wrist and palm vein images." International Journal of Biometrics 11, no. 4 (2019): 309. http://dx.doi.org/10.1504/ijbm.2019.102860.

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Ahmed, Kazi Istiaque, Mohamed Hadi Habaebi, and Md Rafiqul Islam. "A Real Time Vein Detection System." Indonesian Journal of Electrical Engineering and Computer Science 10, no. 1 (2018): 129. http://dx.doi.org/10.11591/ijeecs.v10.i1.pp129-137.

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<p>Blood veins detection process can be cumbersome for nurses and medical practioners when it comes to special overweight type of patients.This simple routine procedure can lead the process into an extreme calamity for these patients. In this paper, we emphasized on a process for the detection of the vein in real time using the consecrations of Matlab to prevent or at least reduce the number of inescapable calamity for patients during the infusion of a needle by phlebotomy or doctor in everyday lives. Hemoglobin of the blood tissues engrossed the Near Infrared (NIR) illuminated light and Night vision camera is used to capture the scene and enhance the vein pattern clearly using Contrast Limited Adaptive Histogram Equalization (CLAHE) method. This simple approach can successfully also lead to localizing bleeding spots, clots from stroke …etc among other things.</p>

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Kazi, Istiaque Ahmed, Hadi Habaebi Mohamed, and Rafiqul Islam Md. "A Real Time Vein Detection System." Indonesian Journal of Electrical Engineering and Computer Science 10, no. 1 (2018): 129–37. https://doi.org/10.11591/ijeecs.v10.i1.pp129-137.

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Blood veins detection process can be cumbersome for nurses and medical practioners when it comes to special overweight type of patients. This simple routine procedure can lead the process into an extreme calamity for these patients. In this paper, we emphasized on a process for the detection of the vein in real time using the consecrations of Matlab to prevent or at least reduce the number of inescapable calamity for patients during the infusion of a needle by phlebotomy or doctor in everyday lives. Hemoglobin of the blood tissues engrossed the Near Infrared (NIR) illuminated light and Night vision camera is used to capture the scene and enhance the vein pattern clearly using Contrast Limited Adaptive Histogram Equalization (CLAHE) method. This simple approach can successfully also lead to localizing bleeding spots, clots from stroke … etc among other things.

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Abd Rahman, Abu Bakar, Floressy Juhim, Fuei Pien Chee, Abdullah Bade, and Fairrul Kadir. "Near Infrared Illumination Optimization for Vein Detection: Hardware and Software Approaches." Applied Sciences 12, no. 21 (2022): 11173. http://dx.doi.org/10.3390/app122111173.

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Venepuncture is one of the most crucial processes in many medical procedures. However, finding a real-time and vibrant visualization of the vein structures faces many difficulties. Several devices were introduced to solve this problem, yet, these devices shared common drawbacks, primarily when visualizing deep veins or veins in a thicker tissue of the human body. This study proposes a novel method for visualizing vein structures using a near-infrared (NIR) imaging technique enhanced with Hessian ridge detection. Several factors, including the wavelength of NIR light, square LED and ring LED arrangement and the effect of the diffuser and number of LEDs, were evaluated in the study. This study improves the overall quality of the acquired vein images and highlights the vein-morphological structure through image processing techniques. The study’s main aim is to achieve the highest number of visible veins. Based on the optical window, the maximum absorption range in the NIR spectrum was found from 700 to 950 nm. The NIR light absorption of human deoxygenated blood in the vein was highest at 850 nm peak of wavelength. The image processing further enhances the vein image by highlighting the extracted vein. The study also suggests that the square LED arrangements of NIR illumination are much more robust than the ring LED arrangement in ensuring excellent light penetration. The light diffuser further adds promising effects to the NIR illumination process. In terms of the square LED arrangement, increasing the square LED for enlarging the illumination area did not show any degradation effects in the visualization process. Overall, this paper presents an integrated hardware and software solution for the NIR image acquisition of a vein visualization system to cope with the image visualization of the vein for a thicker part of the human tissue, particularly on the arm and palm area.

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Wu, Wei, Stephen John Elliott, Sen Lin, and Weiqi Yuan. "Low‐cost biometric recognition system based on NIR palm vein image." IET Biometrics 8, no. 3 (2019): 206–14. http://dx.doi.org/10.1049/iet-bmt.2018.5027.

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K., Santhosh Kumar. "Finger Vein Recognition Using Pattern Matching and Corner Detection Strategies." International Journal of Psychosocial Rehabilitation 24, no. 3 (2020): 2719–33. http://dx.doi.org/10.37200/ijpr/v24i3/pr2020308.

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Lokesh, P. "Embedded system-based enhanced smart security systems for intelligent monitoring applications: A review." i-manager’s Journal on Embedded Systems 12, no. 1 (2023): 16. http://dx.doi.org/10.26634/jes.12.1.20369.

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The advanced smart security system uses palm vein technology and machine learning to enhance authentication. It combines biometric data with behavioral analysis, continuously adapting to improve security. AI integration allows for anomaly detection, distinguishing normal user interactions from suspicious activities. The user-friendly interface makes it accessible for various applications, ensuring resilient protection against evolving threats. The palm vein technology not only enhances security but also minimizes the risk of false positives and negatives, ensuring a reliable and efficient authentication process. In practical scenarios, the proposed system's versatility extends to securing confidential information in various sectors such as finance, research institutions, and government facilities. Its adaptability and compatibility with existing infrastructure make it a seamless and effective solution for organizations seeking to bolster their security measures. Moreover, the system's integration with mobile devices enables users to receive real-time notifications, allowing for prompt action in the event of a security breach. This feature contributes to the overall responsiveness and effectiveness of the security system, especially in remote locations where immediate intervention may be crucial. In conclusion, the advanced smart security system with palm vein technology not only introduces a novel approach to authentication but also addresses the limitations of existing models. The incorporation of machine learning, behavioral analysis, and real-time notifications significantly enhances its overall security features, making it a costeffective and reliable solution for a wide array of applications.

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