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Journal articles on the topic 'Palmprint Recognition'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 July 2025

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1

Kumar, Jayakrishnan S. "Palmprint Recognition in Uncontrolled and Unco-operative Environment." International Journal for Research in Applied Science and Engineering Technology 9, no. 8 (2021): 1711–18. http://dx.doi.org/10.22214/ijraset.2021.37646.

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Abstract: 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, fur

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Zhao, Shuping, Lunke Fei, and Jie Wen. "Multiview-Learning-Based Generic Palmprint Recognition: A Literature Review." Mathematics 11, no. 5 (2023): 1261. http://dx.doi.org/10.3390/math11051261.

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Palmprint recognition has been widely applied to security authentication due to its rich characteristics, i.e., local direction, wrinkle, and texture. However, different types of palmprint images captured from different application scenarios usually contain a variety of dominant features. Specifically, the palmprint recognition performance will be degraded by the interference factors, i.e., noise, rotations, and shadows, while palmprint images are acquired in the open-set environments. Seeking to handle the long-standing interference information in the images, multiview palmprint feature learn

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Al-Taie, Sara A. Mohammed, and Baydaa I. Khaleel. "Palm Print Recognition Using Intelligent Techniques: A review." Jurnal Ilmiah Teknik Elektro Komputer dan Informatika 9, no. 1 (2023): 156–64. https://doi.org/10.26555/jiteki.v9i1.25777.

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Hand or Palm print recognition systems are one of the efficient people recognition and authentication systems that provide high-security levels by approving the entering and exiting of people such as employees in the work field or companies. The basis for using palmprints lies in the fact that no two individuals have exactly the same palmprint pattern, moreover palmprints remain more or less stablethroughout the lifetime and are easily obtainable using standard imaging techniques. Palm print recognition systems process picture data from a photograph of a person's palm and compare it to a recor

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Ma, Ye, and Zhenhua Guo. "Palmprint Translation Network for Cross-Spectral Palmprint Recognition." Electronics 11, no. 5 (2022): 736. http://dx.doi.org/10.3390/electronics11050736.

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Nowadays, palmprint recognition has been well developed since plenty of promising algorithms have emerged. Palmprints have also been applied under various authentication scenarios. However, these approaches are designed and tested only when the registration images and probe images are taken under the same illumination condition; thus, a cross-spectral performance degradation is speculated. Therefore, we test the cross-spectral performance of extended binary orientation co-occurrence vector (E-BOCV), which is unsatisfactory, illustrating the necessity of a specific algorithm. Trying to achieve

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Sen Lin, Sen Lin, Man Li Sen Lin, Meng-Zhao Liu Man Li, Xiang-Ji Fan Meng-Zhao Liu, and Peng Shang Xiang-Ji Fan. "LDBM-PFR: Local Directional Binary Pattern Mixed Filtering for 2D and 3D Palmprint Fusion Recognition." 電腦學刊 35, no. 2 (2024): 059–74. http://dx.doi.org/10.53106/199115992024043502004.

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<p>Single-dimensional palmprint images are susceptible to interference from noise, lighting, and various factors, leading to information loss and subpar recognition performance. To address the problem, a fusion recognition scheme of 2D and 3D palmprints based on Hybrid Filter Local Orientation Binary Pattern (HFLOBP) is proposed. Firstly, the HFLOBP algorithm is employed to extract 2D features and 3D Shape Index (SI) features respectively to improve the recognition effect. Secondly, Principal Component Analysis (PCA) is utilized to improve the recognition efficiency, to reduce the dimens

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Abdulhmid Mohamed Hussin ALBEERISH, Zwha, and Saedah Mohammed Omar ALBEERISH. "PALMPRINT RECOGNITION." MINAR International Journal of Applied Sciences and Technology 2, no. 3 (2020): 21–27. http://dx.doi.org/10.47832/2717-8234.3-2.4.

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Palmprint is an example of physiological characteristics of an individual, which can be easily captured by using some types of sensors and cameras. The palmprint has many nature compositions, which contain rich features that mainly used for distinguishing such as, wrinkles, ridges, principal lines, singular, and minutiae points. These make a palmprint as one of a unique biometric and reliable for human recognition. In this work have used multiple correlation filters per class for performing palmprint classification algorithm

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Taouche, Cherif, Hacene Belhadef, and Zakaria Laboudi. "Palmprint Recognition System Based on Multi-Block Local Line Directional Pattern and Feature Selection." International Journal of Information Technologies and Systems Approach 15, no. 1 (2022): 1–26. http://dx.doi.org/10.4018/ijitsa.292042.

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In this paper, we deal with multimodal biometric systems based on palmprint recognition. In this regard, several palmprint-based approaches have been already proposed. Although these approaches show interesting results, they have some limitations in terms of recognition rate, running time and storage space. To fill this gap, we propose a novel multimodal biometric system combining left and right palmprints. For building this multimodal system, two compact local descriptors for feature extraction are proposed, fusion of left and right palmprints is performed at feature-level, and feature select

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Harun, Nurzalina, Abd Rahman Wan Enyzarina Wan, Sitizaleha Zainal Abidin, and Puwira Jaya Othman. "MODIFIED ALGORITHM OF EXTRACTION OF REGION OF INTEREST (ROI) FOR PALMPRINT IDENTIFICATION." COMPUSOFT: An International Journal of Advanced Computer Technology 07, no. 11 (2018): 2909–15. https://doi.org/10.5281/zenodo.14810809.

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Palmprint trait has emerged as a means of new and practical biometric recognition system. Palmprint is divided into three regions known as interdigital, hypothenar and thenar. These regions contain a bundle of patterns such as creases, ridges, minutiae and pores that are believed to be unique and distinct in establishing the identity of a person. The process of extracting the palmprint ROI is a crucial and important initial process for personal identification. In obtaining palmprint ROI, there are numerous algorithms that have been proposed by past researchers. However, to the best of our know

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Kusban, Muhammad, Aris Budiman, and Bambang Hari Purwoto. "Image enhancement in palmprint recognition: a novel approach for improved biometric authentication." International Journal of Electrical and Computer Engineering (IJECE) 14, no. 2 (2024): 1299. http://dx.doi.org/10.11591/ijece.v14i2.pp1299-1307.

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Several researchers have used image enhancement methods to reduce detection errors and increase verification accuracy in palmprint identification. Divergent opinions exist among experts regarding the best method of image filtering to improve image palmprint recognition. Because of the unique characteristics of palmprints and the difficulties in preventing counterfeiting, image-filtering techniques are the subject of this current research. Researchers hope to create the best biometric system possible by utilizing various techniques. These techniques include image enhancement, Gabor orientation

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Kusban, Muhammad, Aris Budiman, and Bambang Hari Purwoto. "Image enhancement in palmprint recognition: a novel approach for improved biometric authentication." International Journal of Electrical and Computer Engineering (IJECE) 14, no. 2 (2024): 1299–307. https://doi.org/10.11591/ijece.v14i2.pp1299-1307.

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Several researchers have used image enhancement methods to reduce detection errors and increase verification accuracy in palmprint identification. Divergent opinions exist among experts regarding the best method of image filtering to improve image palmprint recognition. Because of the unique characteristics of palmprints and the difficulties in preventing counterfeiting, image-filtering techniques are the subject of this current research. Researchers hope to create the best biometric system possible by utilizing various techniques. These techniques include image enhancement, Gabor orientation

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PRASAD, S. M., V. K. GOVINDAN, and P. S. SATHIDEVI. "AN EFFICIENT WAVELET-BASED PALMPRINT VERIFICATION APPROACH." International Journal of Wavelets, Multiresolution and Information Processing 08, no. 06 (2010): 961–85. http://dx.doi.org/10.1142/s0219691310003833.

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This paper proposes a wavelet-based palmprint verification approach which is efficient in terms of accuracy and speed. The prominent wavelet domain features such as subband energy distribution, histogram, and co-occurrence features fail to characterize the palmprints sufficiently due to coefficient perturbations caused by translational and/or rotational variations in palmprints. In this work, firstly, a novel approach, termed as adaptive tessellation of subbands, is proposed to effectively capture the spatially localized energy distribution based on the spread of principal lines. Secondly, a s

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Wu, Lian, Yong Xu, Zhongwei Cui, Yu Zuo, Shuping Zhao, and Lunke Fei. "Triple-Type Feature Extraction for Palmprint Recognition." Sensors 21, no. 14 (2021): 4896. http://dx.doi.org/10.3390/s21144896.

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Palmprint recognition has received tremendous research interests due to its outstanding user-friendliness such as non-invasive and good hygiene properties. Most recent palmprint recognition studies such as deep-learning methods usually learn discriminative features from palmprint images, which usually require a large number of labeled samples to achieve a reasonable good recognition performance. However, palmprint images are usually limited because it is relative difficult to collect enough palmprint samples, making most existing deep-learning-based methods ineffective. In this paper, we propo

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Li, Jing Wen. "Research on Applied Technology of Palmprint Recognition Based on Fisher Linear Discriminant and Improved PCA Algorithm." Advanced Materials Research 886 (January 2014): 515–18. http://dx.doi.org/10.4028/www.scientific.net/amr.886.515.

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The information applied technology of palmprint recognition is a biometric technology, it’s based on the effective information on the palm (such as: palmprint) to identifies people. The palmprint is unique and characteristic, these are the identification of critical conditions. The feature extraction of palmprint image is a prerequisite for recognition, feature extraction algorithm depends on the quality of the recognition rate and efficiency. This paper presents a method of palmprint recognition algorithm based on Fisher linear discriminant analysis and improved PCA algorithm. The experimenta

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Keerthi, Guttikonda, Devarakonda Uma Katyayani, Balla Naga Durga, Boddu Snehitha, and Chinta Durga Rahul Naidu. "TOWARDS SECURE BIOMETRICS: DEEP FAKE PALMPRINT DETECTION WITH ADVANCED CNN-RESNET MODELS." Journal of Nonlinear Analysis and Optimization 16, no. 01 (2025): 153–61. https://doi.org/10.36893/jnao.2025.v16.i01.020.

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The increasing sophistication of deepfake technology poses a substantial risk to biometric security, particularly in palmprint recognition systems. As palmprint authentication gains widespread adoption in financial transactions and identity verification, the potential for deepfake-based spoofing attacks becomes a major concern. Fraudsters can manipulate or synthetically generate palmprint images to bypass authentication systems, leading to financial fraud and compromised security. This research focuses on the development of an effective deepfake palmprint detection framework leveraging deep le

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Ni, Jianyun, Jing Luo, and Wubin Liu. "3D Palmprint Recognition Using Dempster-Shafer Fusion Theory." Journal of Sensors 2015 (2015): 1–7. http://dx.doi.org/10.1155/2015/252086.

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This paper proposed a novel 3D palmprint recognition algorithm by combining 3D palmprint features using D-S fusion theory. Firstly, the structured light imaging is used to acquire the 3D palmprint data. Secondly, two types of unique features, including mean curvature feature and Gaussian curvature feature, are extracted. Thirdly, the belief function of the mean curvature recognition and the Gaussian curvature recognition was assigned, respectively. Fourthly, the fusion belief function from the proposed method was determined by the Dempster-shafer (D-S) fusion theory. Finally, palmprint recogni

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N. Kohila. "Laplacian Kernel Ruzicka Indexive Deep Multilayer Perceptive Network For Palm Prints Detection Classification." Communications on Applied Nonlinear Analysis 31, no. 8s (2024): 95–114. http://dx.doi.org/10.52783/cana.v31.1459.

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Palmprint is a biometric technology that involves identifying individuals based on the unique patterns and features present in their palmprints. Biometric technology is an important method to enhance security and access control through automated personal authentication. Biometric technologies includes various human traits, such as DNA, fingerprints, faces, iris patterns, palmprints, voice, signatures, and more, to perform personal authentication. Among these human traits, palmprint is an essential biometric technology, attracting significant attention in security systems. Several methods have

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Xiao, Yong Liang. "A Novel Method for Palmprint Recognition Based on Tensor Subspace Learning." Key Engineering Materials 439-440 (June 2010): 1398–403. http://dx.doi.org/10.4028/www.scientific.net/kem.439-440.1398.

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Recently, palmprint identification has been developed for security purpose. In this paper, we propose a novel palmprint recognition scheme which has three features: 1) representation of palmprint images by Local Binary Pattern (LBP); 2) dimensionality reduction by tensor subspace learning; and 3) recognition by multiple kernel classification method based on tensor analysis. LBP can effectively capture substantial palm features while keeping robustness to illumination. Then we reduce the dimensionality of each palmprint samples based on tensor subspace learning which can preserve the spatial st

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Aprilla, Diah Mitha, Ario Yudo Husodo, and I. Gede Pasek Suta Wijaya. "Enhanced Identity Recognition Through the Development of a Convolutional Neural Network Using Indonesian Palmprints." Jurnal Teknik Informatika (Jutif) 6, no. 2 (2025): 521–38. https://doi.org/10.52436/1.jutif.2025.6.2.4169.

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The use of palmprint as an identification system has gained significant attention due to its potential in biometric authentication. However, existing models often face challenges related to computational complexity and the ability to scale with larger datasets. This research aims to develop an efficient Convolutional Neural Network (CNN) model for palmprint identity recognition, specifically tailored to address these challenges. A novel contribution of this study is the creation of an original palmprint dataset consisting of 700 images from 50 Indonesian college students, which serves as a fou

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Attia, Abdelouahab, Abdelouahab Moussaoui, Youssef Chahir, and Mourad Chaa. "ENSEMBLE OF PREPROCESSING TECHNIQUES FOR 3D PALMPRINT RECOGNITION WITH COLLABORATIVE REPRESENTATION BASED CLASSIFICATION." ICTACT Journal on Image and Video Processing 11, no. 1 (2020): 2224–31. https://doi.org/10.21917/ijivp.2020.0319.

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3D Palmprint recognition has become a promising alternative tool for resolving problems compared to the robustness of 2D palmprint recognition. Regarding robustness, biometric systems that use 2D Palmprint suffer from being attacked by using a fake Palmprint identical. Given this, the current paper introduces a new 3D Palmprint recognition approach. Firstly, a set of preprocessing techniques has been applied on 3D depth image such as Tan and Triggs method which can effectively and efficiently eliminate the effect of the low-frequency component with keeping the local statistical properties of t

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Jia, Wei, Wei Xia, Yang Zhao, Hai Min, and Yan-Xiang Chen. "2D and 3D Palmprint and Palm Vein Recognition Based on Neural Architecture Search." International Journal of Automation and Computing 18, no. 3 (2021): 377–409. http://dx.doi.org/10.1007/s11633-021-1292-1.

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AbstractPalmprint recognition and palm vein recognition are two emerging biometrics technologies. In the past two decades, many traditional methods have been proposed for palmprint recognition and palm vein recognition and have achieved impressive results. In recent years, in the field of artificial intelligence, deep learning has gradually become the mainstream recognition technology because of its excellent recognition performance. Some researchers have tried to use convolutional neural networks (CNNs) for palmprint recognition and palm vein recognition. However, the architectures of these C

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A. Khalid, Noor Aldeen, Muhammad Imran Ahmad, Tan Shie Chow, Thulfiqar H. Mandeel, Ibrahim Majid Mohammed, and Mokhalad Abdulameer Kadhim Alsaeedi. "Palmprint recognition system using VR-LBP and KAZE features for better recognition accuracy." Bulletin of Electrical Engineering and Informatics 13, no. 2 (2024): 1060–68. http://dx.doi.org/10.11591/eei.v13i2.4739.

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The palmprint recognition system has gained significant attention in security and law enforcement due to its unique features, such as principle lines, ridges, and wrinkles. However, many existing methods for extracting these features have limited accuracy, especially when the image illumination varies or the size of the processed pixels increases. Previous studies have shown that the local binary patterns (LBP) algorithm is effective for palmprint recognition due to the rich texture characteristics of a palmprint. In this paper, we propose a new technique for a robust contact-based palmprint i

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Fei, Lunke, Shaohua Teng, Jigang Wu, and Imad Rida. "Enhanced Minutiae Extraction for High-Resolution Palmprint Recognition." International Journal of Image and Graphics 17, no. 04 (2017): 1750020. http://dx.doi.org/10.1142/s0219467817500206.

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A palmprint generally possesses about 10 times more minutiae features than a fingerprint, which could provide reliable biometric-based personal authentication. However, wide distribution of various creases in a palmprint creates a number of spurious minutiae. Precisely and efficiently, minutiae extraction is one of the most critical and challenging work for high-resolution palmprint recognition. In this paper, we propose a novel minutiae extraction and matching method for high-resolution palmprint images. The main contributions of this work include the following. First, a circle-boundary consi

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Wu, Qing E., Zhiwu Chen, Ruijie Han, et al. "A Palmprint Recognition Approach Based on Image Segmentation of Region of Interest." International Journal of Pattern Recognition and Artificial Intelligence 30, no. 02 (2016): 1656002. http://dx.doi.org/10.1142/s0218001416560024.

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To carry out an effective recognition for palmprint, this paper presents an algorithm of image segmentation of region of interest (ROI), extracts the ROI of a palmprint image and studies the composing features of palmprint. This paper constructs a coordinate by making use of characteristic points in the palm geometric contour, improves the algorithm of ROI extraction and provides a positioning method of ROI. Moreover, this paper uses the wavelet transform to divide up ROI, extracts the energy feature of wavelet, gives an approach of matching and recognition to improve the correctness and effic

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Amrouni, Nadia, Amir Benzaoui, and Abdelhafid Zeroual. "Palmprint Recognition: Extensive Exploration of Databases, Methodologies, Comparative Assessment, and Future Directions." Applied Sciences 14, no. 1 (2023): 153. http://dx.doi.org/10.3390/app14010153.

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This paper presents a comprehensive survey examining the prevailing feature extraction methodologies employed within biometric palmprint recognition models. It encompasses a critical analysis of extant datasets and a comparative study of algorithmic approaches. Specifically, this review delves into palmprint recognition systems, focusing on different feature extraction methodologies. As the dataset wields a profound impact within palmprint recognition, our study meticulously describes 20 extensively employed and recognized palmprint datasets. Furthermore, we classify these datasets into two di

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Abdullah, Mohan, Beshir Kedir, Kebede Abebe Alemayehu, and Hailu Takore Habtemarium. "A Single Objective GA and PSO for the Multimodal Palmprint Recognition System." Mathematical Problems in Engineering 2023 (January 24, 2023): 1–14. http://dx.doi.org/10.1155/2023/7621550.

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Biometric plays a vital role in human authentication systems. Unimodal and multimodal biometrics have been active research areas for the past few decades. The investigation of palmprint recognition under various illuminations, rotations, and translations is a challenging task. The research work on multimodal palmprint recognition systems has widely increased to improve the recognition rate and reduce execution time. In this article, a multimodal palmprint biometric system is formed by combining the left and right palmprint images to obtain an optimal recognition rate. A modified multilobe ordi

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Wu, Xiangqian, David Zhang, and Kuanquan Wang. "Fisherpalms based palmprint recognition." Pattern Recognition Letters 24, no. 15 (2003): 2829–38. http://dx.doi.org/10.1016/s0167-8655(03)00141-7.

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Xu, Ying, and Fei Luo. "Research and Evaluation of Multispectral Feature Selection and Fusion Model for Palmprint Recognition." Applied Mechanics and Materials 411-414 (September 2013): 1291–94. http://dx.doi.org/10.4028/www.scientific.net/amm.411-414.1291.

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Palmprint is widely used in personal identification for an accurate and robust recognition. Multispectral palmprint images capture under different illumination, including Red, Green, Blue and Infrared maybe contribute to the recognition results. However, the evaluation of selection and fusion of how this different spectral images can contribute to improve the robustness of the recognition system is imperative. In this paper, a novel wavelet-based multispectral fusion strategy is presented firstly to obtain the fused images; then block singular value decomposition (B-SVD) is applied for feature

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Jing, Kunlei, Xinman Zhang, and Guokun Song. "Correntropy-Induced Discriminative Nonnegative Sparse Coding for Robust Palmprint Recognition." Sensors 20, no. 15 (2020): 4250. http://dx.doi.org/10.3390/s20154250.

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Palmprint recognition has been widely studied for security applications. However, there is a lack of in-depth investigations on robust palmprint recognition. Regression analysis being intuitively interpretable on robustness design inspires us to propose a correntropy-induced discriminative nonnegative sparse coding method for robust palmprint recognition. Specifically, we combine the correntropy metric and l1-norm to present a powerful error estimator that gains flexibility and robustness to various contaminations by cooperatively detecting and correcting errors. Furthermore, we equip the erro

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Jin, Jianlong, Lei Shen, Ruixin Zhang, et al. "PCE-Palm: Palm Crease Energy Based Two-Stage Realistic Pseudo-Palmprint Generation." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 3 (2024): 2616–24. http://dx.doi.org/10.1609/aaai.v38i3.28039.

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The lack of large-scale data seriously hinders the development of palmprint recognition. Recent approaches address this issue by generating large-scale realistic pseudo palmprints from Bézier curves. However, the significant difference between Bézier curves and real palmprints limits their effectiveness. In this paper, we divide the Bézier-Real difference into creases and texture differences, thus reducing the generation difficulty. We introduce a new palm crease energy (PCE) domain as a bridge from Bézier curves to real palmprints and propose a two-stage generation model. The first stage gene

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Zhang, Jian Xin, Zhang E. Zhang, and Jian Yang Liu. "Palmprint Recognition Based on Sparse Two-Dimensional Local Discriminant Projections." Advanced Materials Research 998-999 (July 2014): 894–98. http://dx.doi.org/10.4028/www.scientific.net/amr.998-999.894.

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A novel palmprint recognition method based on sparse two-dimensional local discriminant projections (S2DLDP) is proposed. After a description of the basic theory and resolution method for S2DLDP, the paper presents the detail palmprint feature extraction method based on S2DLDP, and tests the algorithm performance by various non-zero elements size and neighborhood size. S2DLDP considerers the class information, local separability, two-dimensional image inherent properties of training samples and sparse projection, which provides an intuitive, semantic and interpretable feature subspace for palm

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Mustafa, Raniah Ali, Haitham Salman Chyad, and Rafid Aedan Haleot. "Palm print recognition based on harmony search algorithm." International Journal of Electrical and Computer Engineering (IJECE) 11, no. 5 (2021): 4113. http://dx.doi.org/10.11591/ijece.v11i5.pp4113-4124.

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Due to its stabilized and distinctive properties, the palmprint is considered a physiological biometric. Recently, palm print recognition has become one of the foremost desired identification methods. This manuscript presents a new recognition palm print scheme based on a harmony search algorithm by computing the Gaussian distribution. The first step in this scheme is preprocessing, which comprises the segmentation, according to the characteristics of the geometric shape of palmprint, the region of interest (ROI) of palmprint was cut off. After the processing of the ROI image is taken as input

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Raniah, Ali Mustafa, Salman Chyad Haitham, and Aedan Haleot Rafid. "Palm print recognition based on harmony search algorithm." International Journal of Electrical and Computer Engineering (IJECE) 11, no. 5 (2021): 4113–24. https://doi.org/10.11591/ijece.v11i5.pp4113-4124.

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Due to its stabilized and distinctive properties, the palmprint is considered a physiological biometric. Recently, palm print recognition has become one of the foremost desired identification methods. This manuscript presents a new recognition palm print scheme based on a harmony search algorithm by computing the Gaussian distribution. The first step in this scheme is preprocessing, which comprises the segmentation, according to the characteristics of the geometric shape of palmprint, the region of interest (ROI) of palmprint was cut off. After the processing of the ROI image is taken as input

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AlShemmary, Ebtesam. "Siamese Network-Based Palm Print Recognition." Journal of Kufa for Mathematics and Computer 10, no. 1 (2023): 108–18. http://dx.doi.org/10.31642/jokmc/2018/100116.

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palm print recognition is a biometric technology used to identify individuals based on their unique comfort patterns. Identifying patterns in computer vision is a challenging and interesting problem. It is an effective and reliable method for authentication and access control. In recent years, deep learning approaches have been used for handprint recognition with very good results. We suggest in this paper, a Siamese network-based approach for handprint recognition. The proposed approach consists of two convolutional neural networks (CNNs) that share weights and are trained to extract features

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Aberni, Yassir, Larbi Boubchir, and Boubaker Daachi. "Multispectral Palmprint Recognition: A Survey and Comparative Study." Journal of Circuits, Systems and Computers 28, no. 07 (2019): 1950107. http://dx.doi.org/10.1142/s021812661950107x.

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Multispectral palmprint recognition has been investigated for many problems and applications over the last decade. It has become one of the most well-known biometric recognition systems. Its success is due to the rich features that can be extracted and exploited from the multispectral images of palmprint captured within specific wavelength ranges across the electromagnetic spectrum. This paper provides an overview of recent state-of-the-art multispectral palmprint approaches for person recognition. The approaches surveyed are discussed by describing, in particular, their feature extraction, fe

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MU, MEIRU, and QIUQI RUAN. "REGION COVARIANCE MATRICES AS FEATURE DESCRIPTORS FOR PALMPRINT RECOGNITION USING GABOR FEATURES." International Journal of Pattern Recognition and Artificial Intelligence 25, no. 04 (2011): 513–28. http://dx.doi.org/10.1142/s0218001411008737.

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Region covariance matrices (RCMs) as feature descriptors have been developed due to the advantages of low dimensionality, being scale and illumination independent. How to define a feature mapping vector for the RCMs construction of strong discriminating ability is still an open issue. In this paper, there is a focus on finding a more efficient feature mapping vector for RCMs as palmprint descriptors based on Gabor magnitude and phase (GMP) information. Specially, Gabor magnitude (GM) features of each palmprint image approximate a lognormal distribution. For palmprint recognition, the logarithm

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Chalabi, Nour Elhouda, Abdelouahab Attia, and Abderraouf Bouziane. "BLOCK WISE 3D PALMPRINT RECOGNITION BASED ON TAN AND TRIGGS WITH BSIF DESCRIPTOR." ICTACT Journal on Soft Computing 11, no. 2 (2021): 2251–59. https://doi.org/10.21917/ijsc.2021.0322.

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Faced by problems such as lack of robustness from 2D palmprint recognition system which can result to be attacked using a fake palmprint or having the same palmprint as another individual, 3D can present an alternative solution to deal with this problem, hence in this paper we are going to introduce a novel approach based on 3D palmprint recognition system named TT-P-BSIF: first, a preprocessing technique based on Tan and Triggs method was applied on a 3D depth image in order to effectively and efficiently eliminate the effect of low frequency component and at the same time keeping the local s

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Wu, Wei, Yuan Zhang, Yunpeng Li, and Chuanyang Li. "Fusion recognition of palmprint and palm vein based on modal correlation." Mathematical Biosciences and Engineering 21, no. 2 (2024): 3129–46. http://dx.doi.org/10.3934/mbe.2024139.

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<abstract> <p>Biometric authentication prevents losses from identity misuse in the artificial intelligence (AI) era. The fusion method integrates palmprint and palm vein features, leveraging their stability and security and enhances counterfeiting prevention and overall system efficiency through multimodal correlations. However, most of the existing multi-modal palmprint and palm vein feature extraction methods extract only feature information independently from different modalities, ignoring the importance of the correlation between different modal samples in the class to the impr

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Liu, Yu Qin, Wei Qi Yuan, and Jin Yu Guo. "Block Statistic for Palmprint Recognition Based on High Frequency Coefficients under Wavelet Transform." Applied Mechanics and Materials 182-183 (June 2012): 1287–91. http://dx.doi.org/10.4028/www.scientific.net/amm.182-183.1287.

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Palmprint recognition for identification provides a new scheme for information security. This paper presents a block statistic method based on high frequency coefficients under wavelet transform for palmprint identification. Firstly, the method decomposes region of interest (ROI) of the palmprint with the wavelet transform. Then it blocks the high-frequency sub-image. The mean and the variance for each sub-block are found. All the means and the variances constitute feature vector for the image. At last the nearest neighbor classifier is used to classify the images. The method was tested on the

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Laadjel, Moussadek, Ahmed Bouridane, Fatih Kurugollu, and WeiQi Yan. "Palmprint Recognition Based on Subspace Analysis of Gabor Filter Bank." International Journal of Digital Crime and Forensics 2, no. 4 (2010): 1–15. http://dx.doi.org/10.4018/jdcf.2010100101.

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This paper introduces a new technique for palmprint recognition based on Fisher Linear Discriminant Analysis (FLDA) and Gabor filter bank. This method involves convolving a palmprint image with a bank of Gabor filters at different scales and rotations for robust palmprint features extraction. Once these features are extracted, FLDA is applied for dimensionality reduction and class separability. Since the palmprint features are derived from the principal lines, wrinkles and texture along the palm area. One should carefully consider this fact when selecting the appropriate palm region for the fe

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Yuan Weiqi, 苑玮琦, 曲晓峰 Qu Xiaofeng, 柯丽 Ke Li, and 黄静 Huang Jing. "PCA Reconstruction Error Palmprint Recognition." Acta Optica Sinica 28, no. 10 (2008): 1903–9. http://dx.doi.org/10.3788/aos20082810.1903.

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YUE, Feng, Wang-Meng ZUO, and Da-Peng ZHANG. "Survey of Palmprint Recognition Algorithms." Acta Automatica Sinica 36, no. 3 (2010): 353–65. http://dx.doi.org/10.3724/sp.j.1004.2010.00353.

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Jia, Wei, Rong-Xiang Hu, Jie Gui, Yang Zhao, and Xiao-Ming Ren. "Palmprint Recognition across Different Devices." Sensors 12, no. 6 (2012): 7938–64. http://dx.doi.org/10.3390/s120607938.

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Zhang, Lin, Hongyu Li, and Junyu Niu. "Fragile Bits in Palmprint Recognition." IEEE Signal Processing Letters 19, no. 10 (2012): 663–66. http://dx.doi.org/10.1109/lsp.2012.2211589.

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Pusia Putra, Gede Ngurah Pasek, Ketut Gede Darma Putra, and Putu Wira Buana. "Android Based Palmprint Recognition System." TELKOMNIKA (Telecommunication Computing Electronics and Control) 12, no. 1 (2014): 263. http://dx.doi.org/10.12928/telkomnika.v12i1.1993.

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Pusia Putra, Gede Ngurah Pasek, Ketut Gede Darma Putra, and Putu Wira Buana. "Android Based Palmprint Recognition System." TELKOMNIKA (Telecommunication Computing Electronics and Control) 12, no. 1 (2014): 263. http://dx.doi.org/10.12928/telkomnika.v12i1.28.

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Lu, Guangming, David Zhang, and Kuanquan Wang. "Palmprint recognition using eigenpalms features." Pattern Recognition Letters 24, no. 9-10 (2003): 1463–67. http://dx.doi.org/10.1016/s0167-8655(02)00386-0.

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Kong, Adams, David Zhang, and Mohamed Kamel. "A survey of palmprint recognition." Pattern Recognition 42, no. 7 (2009): 1408–18. http://dx.doi.org/10.1016/j.patcog.2009.01.018.

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Cui, Jinrong. "Multispectral fusion for palmprint recognition." Optik - International Journal for Light and Electron Optics 124, no. 17 (2013): 3067–71. http://dx.doi.org/10.1016/j.ijleo.2012.09.030.

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Connie, Tee, Andrew Teoh Beng Jin, Michael Goh Kah Ong, and David Ngo Chek Ling. "An automated palmprint recognition system." Image and Vision Computing 23, no. 5 (2005): 501–15. http://dx.doi.org/10.1016/j.imavis.2005.01.002.

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Dubey, Pawan, Tirupathiraju Kanumuri, and Ritesh Vyas. "Sequency codes for palmprint recognition." Signal, Image and Video Processing 12, no. 4 (2017): 677–84. http://dx.doi.org/10.1007/s11760-017-1207-3.

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