Academic literature on the topic 'Palm vein pattern detection'

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Journal articles on the topic "Palm vein pattern detection"

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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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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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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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Dissertations / Theses on the topic "Palm vein pattern detection"

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Dohnálek, Tomáš. "Liveness Detection on Fingers Using Vein Pattern." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2015. http://www.nusl.cz/ntk/nusl-234901.

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Tato práce se zabývá rozšířením snímače otisků prstů Touchless Biometric Systems 3D-Enroll o jednotku detekce živosti prstu na základě žil. Bylo navrhnuto a zkonstruováno hardwarové řešení s využitím infračervených diod. Navržené softwarové řešení pracuje ve dvou různých režimech: detekce živosti na základě texturních příznaků a verifikace uživatelů na základě porovnávání žilních vzorů. Datový soubor obsahující přes 1100 snímků jak živých prstů tak jejich falsifikátů vznikl jako součást této práce a výkonnost obou zmíněných režimů byla vyhodnocena na tomto datovém souboru. Na závěr byly navrhnuty materiály vhodné k výrobě falsifikátů otisků prstů umožňující oklamání detekce živosti pomocí žilních vzorů.
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Yang, Shiuh-Pyng, and 楊緒屏. "Back-palm recognition based on vein pattern." Thesis, 2009. http://ndltd.ncl.edu.tw/handle/92783243743745868385.

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碩士<br>國立高雄應用科技大學<br>光電與通訊研究所<br>97<br>For the characteristic of uniqueness on the human, number of biometric technologies have been developed and are used in personal identification like as iris, fingerprint, face detection and vein etc. The back-palm vein pattern is unique to individuality which pattern does not change over time apart from size. This feature makes it suitable for one-to-many matching, for which fingerprint and face recognition may not be robust. Among all the biometric techniques, back-palm vein recognition is a topic worthy to receive further investigation since this technology overcomes aversion to fingerprinting and related privacy concerns, which its traditional association to criminal activity is non-existent. Back-palm vein recognition works by identifying the subcutaneous vein patterns in an individual's back-palm and is difficult to replicate because they lie under the skin surface. When a user's back-palm is placed under the color SONY camera (SSC-E473) in our system, a near-infrared (NIR) light maps the location of the veins. The red blood cells present in the veins absorb the rays and show up on the map as black rugged lines, whereas the remaining back-palm structure shows up as light cyan. After the vein classification, it is compared with previously stored patterns and a match is made. In this thesis, we present a new method for vein extraction and classification of the back-palm vein. High-boost filter enhanced original blur image with NIR radiation CCD (charged-couple-device) camera. For enhancing vein data, a novel thresholding is designed to separate foreground data and background information. A 11×11 mask from Laplacian of Gaussian concept contrast between vein and surrounding areas. For avoiding the contour of the back palm affect the vein extraction, median filter is used priority to remove most of the noise. We use AND gate to contrast the result between binarization and median image. Thresholding of 3×3 mask is utilized to clear the noise around contour of the back-palm image. Sequentially, morphological opening is used to eliminate little noise before vein extraction with connected component labeling. After above movement, morphological closing, thinning and pruning present skeleton and clear protrude on the skeleton image. Finally, both positive and opposite Y types are designed to find out the tri-intersection in pruning image, then utilize binary tree and counting independent numbers about positive and opposite Y types for same number in classified file to classification. This method can achieve stable classification and original recognizable purpose. For reducing the recognizable error in the future, we design average coordinate method to set up three datum points with positive Y, opposite Y, total Y and calculate independent score with mutual Euclidean distance between average coordinate and Y points. In our experiment, 25 enrolled user (five girls and twenty boys) of different gender, each has both 10 images for left and right hand at different intervals. The performance of the accurate extraction ratio is 93.35% (94.8% on right hand, 91.9% on left hand) between back-palm vein pattern and original images.
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楊子毅. "The study of pattern extraction and recognition based on finger-vein and palm-print." Thesis, 2006. http://ndltd.ncl.edu.tw/handle/09751773561597304903.

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Book chapters on the topic "Palm vein pattern detection"

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Zhong, Dexing, Shuming Liu, Wenting Wang, and Xuefeng Du. "Palm Vein Recognition with Deep Hashing Network." In Pattern Recognition and Computer Vision. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-03398-9_4.

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Qin, Huafeng, and Mounîm A. El Yacoubi. "End-to-End Generative Adversarial Network for Palm-Vein Recognition." In Pattern Recognition and Artificial Intelligence. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-59830-3_62.

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Balaraja, E., and M. K. Mariam Bee. "Finger Vein Pattern Detection Using Neuro Fuzzy System." In Advances in Intelligent Systems and Computing. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-15-9927-9_38.

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Damak, Wafa, Randa Boukhris Trabelsi, Alima Damak Masmoudi, and Dorra Sellami. "Palm Vein Age and Gender Estimation Using Center Symmetric-Local Binary Pattern." In Advances in Intelligent Systems and Computing. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-20005-3_12.

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Kharakwal, Komal, Y. P. Raiwani, and Rohan Verma. "A Review on Skin Pigment and Vein Pattern Detection Techniques." In Communications in Computer and Information Science. Springer Nature Switzerland, 2022. http://dx.doi.org/10.1007/978-3-031-24367-7_5.

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Goutham, V., D. L. Lakshmi, M. K. Hamsashree, B. Naveen, and D. L. Girijamba. "A Review on Detection of Vein Pattern in Human Body for the Biometric Applications." In Communications in Computer and Information Science. Springer Nature Switzerland, 2022. http://dx.doi.org/10.1007/978-3-031-22405-8_1.

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"The Panoramic Views of Cloud IoT-Based M-Health Biometrics." In Cloud-Based M-Health Systems for Vein Image Enhancement and Feature Extraction. IGI Global, 2021. http://dx.doi.org/10.4018/978-1-7998-4537-9.ch001.

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The veins-based biometric systems use the molds and patterns of the veins' images of the human body for identification in standalone systems or a cloud internet of things (IoT)-based networking environment. The beauty of using veins-based systems for identification is that the vein pattern cannot be stolen or duplicated or washed out because of its availability in the human body. Currently, vein patterns of fingers, hand, palm, heart, head, palm-dorsa, and wrist of humans are used for biometric identification purposed in cloud and IoT-based network environments. In this chapter, the authors have described different types of algorithms including parallel algorithms for identifying persons in clouds and IoT-based environments. The authors observed that many researchers have designed and developed several algorithms to improve and extract the veins patterns from different parts of the human body for identification in different types of environments including clouds and the internet of things.
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Singh, Aradhana Kumari. "Hidden Markov Model for Gesture Recognition." In Challenges and Applications for Hand Gesture Recognition. IGI Global, 2022. http://dx.doi.org/10.4018/978-1-7998-9434-6.ch006.

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In this chapter, hidden Markov model (HMM) is used to apply for gesture recognition. The study comprises the design, implementation, and experimentation of a system for making gestures, training HMMs, and identifying gestures with HMMs in order to better understand the behaviour of hidden Markov models (HMMs). One person extends his flattened, vertical palm toward the other, as though to reassure the other that his hands are safe. The other individual smiles and responds in kind. This wave gesture has been associated with friendship from childhood. Human motions can be thought of as a pattern recognition challenge. A computer can deduce the sender's message and reply appropriately if it can detect and recognise a set of gestures. By creating and implementing a gesture detection system based on the semi-continuous hidden Markov model, this chapter aims to bridge the visual communication gap between computers and humans.
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Conference papers on the topic "Palm vein pattern detection"

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Rastogi, Swati, Siddhartha P. Duttagupta, Anirban Guha, and Surya Prakash. "NIR Palm Vein Pattern Recognition." In 2020 IEEE International Conference for Innovation in Technology (INOCON). IEEE, 2020. http://dx.doi.org/10.1109/inocon50539.2020.9298421.

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Rastogi, Swati, Siddhartha P. Duttagupta, Anirban Guha, and Surya Prakash. "Palm vein pattern: Extraction and Authentication." In 2020 IEEE International Conference on Machine Learning and Applied Network Technologies (ICMLANT). IEEE, 2020. http://dx.doi.org/10.1109/icmlant50963.2020.9355986.

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Kurbatova, Ekaterina, Natalya Kharina, Anton Zemtsov, and Stepan Plyaskin. "Investigating Palm Vein Pattern Recognition Methods." In 2022 24th International Conference on Digital Signal Processing and its Applications (DSPA). IEEE, 2022. http://dx.doi.org/10.1109/dspa53304.2022.9790783.

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Iula, Antonio, Alessandro Savoia, and Giosue Caliano. "3D Ultrasound palm vein pattern for biometric recognition." In 2012 IEEE International Ultrasonics Symposium. IEEE, 2012. http://dx.doi.org/10.1109/ultsym.2012.0611.

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Rabie, Sherok M., Hala M. Ebied, and Sahar Bayoumi. "Analysis of Dorsal Palm Vein Pattern Recognition System." In 2019 Ninth International Conference on Intelligent Computing and Information Systems (ICICIS). IEEE, 2019. http://dx.doi.org/10.1109/icicis46948.2019.9014833.

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Bathirappan, Kalaimathi, Alamelu alias Rajasree S, Kowsalya M, Shimona S, and Subashri N. R. "Palm Vein Detection Based on Deep Learning." In 2024 International Conference on Advances in Modern Age Technologies for Health and Engineering Science (AMATHE). IEEE, 2024. http://dx.doi.org/10.1109/amathe61652.2024.10582161.

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Akbar, Akhmad Faizal, Tjokorda Agung Budi Wirayudha, and Mahmud Dwi Sulistiyo. "Palm vein biometric identification system using local derivative pattern." In 2016 4th International Conference on Information and Communication Technology (ICoICT). IEEE, 2016. http://dx.doi.org/10.1109/icoict.2016.7571956.

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Vats, Pranjal, and Siva Kumar Tadepalli. "Palm Vein Image Processing and Enhancement in Vein Pattern Recognition System on FPGA." In 2022 2nd International Conference on Emerging Frontiers in Electrical and Electronic Technologies (ICEFEET). IEEE, 2022. http://dx.doi.org/10.1109/icefeet51821.2022.9848282.

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Salazar-Jurado, Edwin H., Ruber Hernández-García, Karina Vilches Ponce, and Ricardo J. Barrientos. "Mathematical Palm Vein Modeling for Large-Scale Biometric Recognition." In 2023 IEEE 13th International Conference on Pattern Recognition Systems (ICPRS). IEEE, 2023. http://dx.doi.org/10.1109/icprs58416.2023.10179063.

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Raut, Shriram D., and Vikas T. Humbe. "Palm vein recognition system based on corner point detection." In 2015 IEEE International WIE Conference on Electrical and Computer Engineering (WIECON-ECE). IEEE, 2015. http://dx.doi.org/10.1109/wiecon-ece.2015.7443978.

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Reports on the topic "Palm vein pattern detection"

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Varastehpour, Soheil, Hamid Sharifzadeh, Iman Ardekani, and Abdolhossein Sarrafzadeh. Human Biometric Traits: A Systematic Review Focusing on Vascular Patterns. Unitec ePress, 2020. http://dx.doi.org/10.34074/ocds.086.

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Authentication methods based on human traits, including fingerprint, face, iris, and palm print, have developed significantly, and currently they are mature enough to be reliably considered for human identification purposes. Recently, as a new research area, a few methods based on non-facial skin features such as vein patterns have been developed. This literature review paper explores some key biometric systems such as face recognition, iris recognition, fingerprint, and palm print, and discusses their respective advantages and disadvantages; then by providing a comprehensive analysis of these traits, and their applications, vein pattern recognition is reviewed.
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