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Journal articles on the topic 'PALM PRINT RECOGNITION'

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Author: Grafiati

Published: 11 September 2023

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1

Goh, Michael K. O., Connie Tee, and Andrew B. J. Teoh. "BI-MODAL PALM PRINT AND KNUCKLE PRINT RECOGNITION SYSTEM." Journal of IT in Asia 3, no. 1 (April 20, 2016): 85–106. http://dx.doi.org/10.33736/jita.37.2010.

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This paper proposed an innovative contact-less palm print and knuckle print recognition system. Palm print is referred to as line textures, which contains principal lines, wrinkles and ridges on the inner surface of the palm. On the other hand, knuckle print is denoted as the flexion lines on the inner skin of the knuckles of the fingers. These line patterns are unique and stable, and they offer abundance of useful information for personal recognition. We present a novel palm print and knuckle print tracking approach to automatically detect and capture these features from low resolution video stream. No constraint is imposed and the subject can place his/her hand naturally on top of the input sensor without touching any device. The palm print and knuckle print features are extracted using our proposed Wavelet Gabor Competitive Code and Ridget Transform methods. Several decision-level fusion rules are used to consolidate the scores output by the palm print and knuckle print experts. The fusion of these features yields promising result of EER=1.25% for verification rate.

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2

AlShemmary, Ebtesam. "Siamese Network-Based Palm Print Recognition." Journal of Kufa for Mathematics and Computer 10, no. 1 (March 31, 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 from images of handprints, which are then compared using a loss of variance function to determine whether the two images belong to the same person or not. Among 13,982 input images, 20% are used for testing, 80% for training, and then passing each image over one of two matching subnets (CNN) that transmit weights and parameters. So that, the extracted features become clearer and more prominent. This approach has been tested and implemented using the CASIA PalmprintV1 5502 palm print database, the CASIA Multi-Spectral PalmprintV1 7,200 palm print, and the THUPALMLAB database of 1,280 palm print using MATLAB 2022a. For 13,982 palmprint recognitions in the database, the equal error rate was 0.044, and the accuracy was 95.6% (CASIA palmprintV1, THUPALMLAB, and CASIA Multi-Spectral palmprintV1). The performance of the real-time detecting system is stable and fast enough.

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Pushpa, N. B., and N. B. Prajwala. "A Scientific Analysis to Observe Uniqueness in Lip Print Pattern." International Journal of Innovative Technology and Exploring Engineering 10, no. 4 (February 28, 2021): 196–98. http://dx.doi.org/10.35940/ijitee.d8571.0210421.

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Every individual have their unique identification like palm print, signature, finger print, face recognition, lip print etc.. here in this research one such effort is made to analyses lip print and identify the individual using their lip print. The wrinkle and grooves pattern on the lips has individual characteristics like tongue prints, face recognition, iris pattern, fingerprints. Cheiloscopy is a forensic investigation technique that deals with identification of humans based on lips traces. Image processing technique is used, lip print of the individual is captured, processed and analyzed for conclusion.

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4

Karar, Subhajit, and Ranjan Parekh. "Palm Print Recognition using Zernike Moments." International Journal of Computer Applications 55, no. 16 (October 20, 2012): 15–19. http://dx.doi.org/10.5120/8839-3069.

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5

Su, Ching-Liang. "Palm-print recognition by matrix discriminator." Expert Systems with Applications 36, no. 7 (September 2009): 10259–65. http://dx.doi.org/10.1016/j.eswa.2009.01.052.

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6

Badrinath, G. S., and Phalguni Gupta. "Stockwell transform based palm-print recognition." Applied Soft Computing 11, no. 7 (October 2011): 4267–81. http://dx.doi.org/10.1016/j.asoc.2010.05.031.

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7

ATTAR SHAGUSTHA BANU and N VINOD KUMAR. "IMPLEMENTATION OF ACCURATE PERSONAL IDENTIFICATION BY USING PALM PRINT IMAGE PROCESSING." international journal of engineering technology and management sciences 7, no. 1 (February 28, 2023): 120–30. http://dx.doi.org/10.46647/ijetms.2023.v07i01.020.

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Online palmprint recognition and latent palmprint identification are two branches of palmprint studies. The former uses middle-resolution images collected by a digital camera in a well-controlled or contact-based environment with user cooperation for commercial applications and the latter uses highresolution latent palmprints collected in crime scenes for forensic investigation. However, these two branches do not cover some palmprint images which have the potential for forensic investigation. Due to the prevalence of smartphone and consumer camera, more evidence is in the form of digital images taken in uncontrolled and uncooperative environment, e.g., child pornographic images and terrorist images, where the criminals commonly hide or cover their face. However, their palms can be observable. Among various biometrics technologies, palm-print identification has received much attention because of its good performance. Combining the left and right palm-print images to perform multi-biometrics is easy to implement and can obtain better results. Existing systems deployed Line Based Method, Coding Based Method, Subspace Based Methods, Representation Based Method, SIFT Based Method. This work integrated three kinds of scores generated from the left and right palm-print images to perform matching score-level fusion. The first two kinds of scores were, respectively, generated from the left and right palm-print images and can be obtained by any palm-print identification method, whereas the third kind of score was obtained using a specialized algorithm proposed in this paper. As the proposed algorithm carefully takes the nature of the left and right palm-print images into account, it can properly exploit the similarity of the left and right palm-prints of the same subject. Moreover, the proposed weighted fusion scheme allowed perfect identification performance to be obtained in comparison with previous palm-print identification methods.

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8

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 (October 1, 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 related to the harmony search algorithm for extracting the features of the palmprint images through using many parameters for the harmony search algorithm, Finally, Gaussian distribution has been used for computing distance between features for region palm print images, in order to recognize the palm print images for persons by training and testing a set of images, The scheme which has been proposed using palmprint databases, was provided by College of Engineering Pune (COEP), the Hong Kong Polytechnic University (HKPU), Experimental results have shown the effectiveness of the suggested recognition system for palm print with regards to the rate of recognition that reached approximately 92.60%.

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9

Jaafar, Haryati, Salwani Ibrahim, and Dzati Athiar Ramli. "A Robust and Fast Computation Touchless Palm Print Recognition System Using LHEAT and the IFkNCN Classifier." Computational Intelligence and Neuroscience 2015 (2015): 1–17. http://dx.doi.org/10.1155/2015/360217.

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Mobile implementation is a current trend in biometric design. This paper proposes a new approach to palm print recognition, in which smart phones are used to capture palm print images at a distance. A touchless system was developed because of public demand for privacy and sanitation. Robust hand tracking, image enhancement, and fast computation processing algorithms are required for effective touchless and mobile-based recognition. In this project, hand tracking and the region of interest (ROI) extraction method were discussed. A sliding neighborhood operation with local histogram equalization, followed by a local adaptive thresholding or LHEAT approach, was proposed in the image enhancement stage to manage low-quality palm print images. To accelerate the recognition process, a new classifier, improved fuzzy-basedknearest centroid neighbor (IFkNCN), was implemented. By removing outliers and reducing the amount of training data, this classifier exhibited faster computation. Our experimental results demonstrate that a touchless palm print system using LHEAT and IFkNCN achieves a promising recognition rate of 98.64%.

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10

Michael, Goh Kah Ong, Tee Connie, and Andrew Teoh Beng Jin. "An innovative contactless palm print and knuckle print recognition system." Pattern Recognition Letters 31, no. 12 (September 2010): 1708–19. http://dx.doi.org/10.1016/j.patrec.2010.05.021.

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11

Sharma, Vivek kr, and Nisha Vasudeva. "A Review on Palm Print Recognition Techniques." International Journal of Advanced Research in Computer Science and Software Engineering 7, no. 3 (March 30, 2017): 196–201. http://dx.doi.org/10.23956/ijarcsse/v7i3/0169.

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12

Wang Hao, 王浩, 康文雄 Kang Wenxiong, and 陈晓鹏 Chen Xiaopeng. "Palm Print and Palm Vein Joint Recognition System Based Video." Acta Optica Sinica 38, no. 2 (2018): 0215004. http://dx.doi.org/10.3788/aos201838.0215004.

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13

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 (April 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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14

Kumar, V. D. Ambeth, V. D. Ashok Kumar, S. Malathi, and P. Jagaeedesh. "Intruder Identification Using Footprint Recognition with PCA and SVM Classifiers." Advanced Materials Research 984-985 (July 2014): 1345–49. http://dx.doi.org/10.4028/www.scientific.net/amr.984-985.1345.

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In modern digital era authentication has been done using biometric recognition. This biometric includes finger print, footprint, facial recognition, DNA of a person’s gene, hand palm print and eye’s iris recognition. The widely used among these is finger print and iris recognition. In this work we proposed a biometric recognition using footprints of a person. Earlier work deals with capturing footprint on a paper or on a surface. This won’t give us accurate foot print, since it depends on nature of the surface, quality of the paper and proper placement of the foot to give good foot print impression. To avoid all these we proposed a touch less method to obtain foot prints. The footprint can be obtained using any digital camera. We can take footprint image in many angles to conform the individuality of a person. In this work we used Principle Component Analysis (PCA) for pattern recognition and feature extraction. Then the SVM classifier split the patterns in to relevant classes. In early stage of our work itself we got remarkable quality and it is comparatively better than conventional footprint images obtained using paper or surface

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15

M, Rajeshwari, and Rathika K. "Palm Print Recognition Using Texture and Shape Features." International Journal of Computer Science and Engineering 9, no. 2 (February 25, 2022): 1–5. http://dx.doi.org/10.14445/23488387/ijcse-v9i2p101.

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Attallah, Bilal, Amina Serir, Youssef Chahir, and Abdelwahhab Boudjelal. "Superpixel-based Zernike moments for palm-print recognition." International Journal of Electronic Security and Digital Forensics 11, no. 4 (2019): 420. http://dx.doi.org/10.1504/ijesdf.2019.10021735.

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Attallah, Bilal, Amina Serir, Youssef Chahir, and Abdelwahhab Boudjelal. "Superpixel-based Zernike moments for palm-print recognition." International Journal of Electronic Security and Digital Forensics 11, no. 4 (2019): 420. http://dx.doi.org/10.1504/ijesdf.2019.102561.

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18

Rajagopal, Gayathri, and Senthil Kumar Manoharan. "Personal Authentication Using Multifeatures Multispectral Palm Print Traits." Scientific World Journal 2015 (2015): 1–11. http://dx.doi.org/10.1155/2015/861629.

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Biometrics authentication is an effective method for automatically recognizing a person’s identity with high confidence. Multispectral palm print biometric system is relatively new biometric technology and is in the progression of being endlessly refined and developed. Multispectral palm print biometric system is a promising biometric technology for use in various applications including banking solutions, access control, hospital, construction, and forensic applications. This paper proposes a multispectral palm print recognition method with extraction of multiple features using kernel principal component analysis and modified finite radon transform. Finally, the images are classified using Local MeanK-Nearest Centroid Neighbor algorithm. The proposed method efficiently accommodates the rotational, potential deformations and translational changes by encoding the orientation conserving features. The proposed system analyses the hand vascular authentication using two databases acquired with touch-based and contactless imaging setup collected from multispectral Poly U palm print database and CASIA database. The experimental results clearly demonstrate that the proposed multispectral palm print authentication obtained better result compared to other methods discussed in the literature.

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19

M, Arun, Mridula S, and Kamalam M. "Raspberry Pi Based Palm Print Recognition for Person Authentication." Journal of Analog and Digital Devices 6, no. 1 (April 24, 2021): 27–36. http://dx.doi.org/10.46610/joadd.2021.v06i01.005.

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20

Nagajyothi, Dhulipalla, and K. Venkata Ramaiah. "Texture based Palm Print Recognition using Discrete Wavelet Transformation." International Journal of Computer Applications 184, no. 3 (March 26, 2022): 40–43. http://dx.doi.org/10.5120/ijca2022921991.

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21

V, L, Janani, and Hanapra veena. "ATM Transaction Security System Using Biometric Palm Print Recognition." IOSR Journal of Electronics and Communication Engineering 9, no. 5 (2014): 06–11. http://dx.doi.org/10.9790/2834-09510611.

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22

Alobaidy, Muhamad Azhar Abdilatef, Zead Mohammed Yosif, and Ahmed Mamoon Alkababchi. "Age-Dependent Palm Print Recognition Using Convolutional Neural Network." Revue d'Intelligence Artificielle 37, no. 03 (June 30, 2023): 795–800. http://dx.doi.org/10.18280/ria.370328.

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23

Fang, Wang. "The Design of Library Management System Based on Internet of Things and Hand Lines Recognition System." Applied Mechanics and Materials 672-674 (October 2014): 1985–90. http://dx.doi.org/10.4028/www.scientific.net/amm.672-674.1985.

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For an ordinary individual biometric systems and technology, such as fingerprint recognition, palm recognition, face recognition or iris recognition, or late detection from a single object has crippled so that they have the characteristics of unity and limitations, this paper combining fingerprint and hand palm pattern recognition technology, taking into account the complexity of the image pattern and diversity, we propose a dual recognition algorithm, which greatly makes up for lack of a single fingerprint or palm print recognition method. The technology used in library management system than traditional card-borrowed books have higher efficiency and save manpower and material resources. After the experimental statistics, and achieved the desired results, not only improve the recognition efficiency, but also to ensure the accuracy of the recognition performance.

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24

Han, Chin-Chuan, Hsu-Liang Cheng, Chih-Lung Lin, and Kuo-Chin Fan. "Personal authentication using palm-print features." Pattern Recognition 36, no. 2 (February 2003): 371–81. http://dx.doi.org/10.1016/s0031-3203(02)00037-7.

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25

Dangi, Neha, and Atul Barve. "Palm Print Recognition using Steerable Filter for Better Authentication System." International Journal of Computer Applications 182, no. 15 (September 17, 2018): 14–18. http://dx.doi.org/10.5120/ijca2018917796.

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26

Poonia, Poonam, and Pawan K. Ajmera. "Palm-print recognition based on quality estimation and feature dimension." International Journal of Computational Science and Engineering 25, no. 2 (2022): 116. http://dx.doi.org/10.1504/ijcse.2022.10046250.

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et al., Fawzy. "Palm print recognition system using siamese network and transfer learning." International Journal of ADVANCED AND APPLIED SCIENCES 9, no. 3 (March 2022): 90–99. http://dx.doi.org/10.21833/ijaas.2022.03.011.

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This paper proposes a palmprint authentication approach using a one-shot learning technique based on similarity instead of classification (used by most other proposals). The one-shot learning technique uses the siamese network architecture built on top of the pre-trained VGG16 to efficiently reduce the cost and time of training the siamese network. This technique allows the user registration using only one palmprint and then performs the authentication process by performing a siamese similarity measure instead of classification techniques. The proposed model achieved high accuracies scores of 97%, 96.7% for Tongji datasets, 92.3%, 91.9% for PolyU-IITD datasets, 90.9%, 88.3% for CASIA datasets and 95.5% for COEP dataset. These performances were measured based on the testing dataset for unseen persons while the siamese training dataset was applied to different persons. The proposed model uses the pre-trained part of VGG16 as a feature extraction part then feeds the generated feature vector into the Euclidean distance layer that is trained in conjunction with the sigmoid layer to output the final similarity decision. Compared to other models, this proposed model achieved a high average accuracy of 93.2% and 0.19 EER over the available four palm print datasets which is generalized over proposals. All codes are open-source and available online at https://github.com/ProjectsRebository/PalmPrint-recognition-using-Transfer-Learning.

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Poonia, Poonam, and Pawan K. Ajmera. "Palm-print recognition based on quality estimation and feature dimension." International Journal of Computational Science and Engineering 25, no. 2 (2022): 116. http://dx.doi.org/10.1504/ijcse.2022.122204.

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29

Yashodha, G., and R. Bremananth. "An efficient feature extraction technique for palm-print recognition system." International Journal of Biomedical Engineering and Technology 25, no. 2/3/4 (2017): 227. http://dx.doi.org/10.1504/ijbet.2017.087726.

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Yashodha, G., and R. Bremananth. "An efficient feature extraction technique for palm-print recognition system." International Journal of Biomedical Engineering and Technology 25, no. 2/3/4 (2017): 227. http://dx.doi.org/10.1504/ijbet.2017.10008620.

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بشار عبد الستار يونس and مهى عبدالرحمن حسو. "تمييز الأشخاص باعتماد راحة اليد باستخدام خوارزمية التفصيلات." Tikrit Journal of Pure Science 23, no. 5 (January 18, 2023): 155–63. http://dx.doi.org/10.25130/tjps.v23i5.599.

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The palm print Contains main and secondary lines has many important features, which make it one of the important characteristics of biometric with possessing high acceptance by users for recognition. In this paper , a new method is proposed to identify individuals using palm print recognition based on details of minutiae. In the beginning the main and secondary lines were extracted using the maximum curvature method. Then the minutiae points ( Ends and Bifurcations) were extracted by convolute windows(3*3) with the points of lines to determine these points (minutiae). The modified hausdorff distance MHD algorithm is applied to compare the input templates with database that need to identify or verify persons. Many matching process were applied and many thresholds were studied to obtain the best recognition which reaches 99% for 65 threshold, which proved the efficiency of the proposed algorithm very high accuracy in recognition and decrease of error rates.

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32

Al-Mahafzah, Harbi, Tamer AbuKhalil, Malek Alksasbeh, and Bassam Alqaralleh. "Multi-modal palm-print and hand-vein biometric recognition at sensor level fusion." International Journal of Electrical and Computer Engineering (IJECE) 13, no. 2 (April 1, 2023): 1954. http://dx.doi.org/10.11591/ijece.v13i2.pp1954-1963.

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When it is important to authenticate a person based on his or her biometric qualities, most systems use a single modality (e.g. fingerprint or palm print) for further analysis at higher levels. Rather than using higher levels, this research recommends using two biometric features at the sensor level. The Log-Gabor filter is used to extract features and, as a result, recognize the pattern, because the data acquired from images is sampled at various spacing. Using the two fused modalities, the suggested system attained greater accuracy. Principal component analysis was performed to reduce the dimensionality of the data. To get the optimum performance between the two classifiers, fusion was performed at the sensor level utilizing different classifiers, including <em>k</em>-nearest neighbors and support vector machines. The technology collects palm prints and veins from sensors and combines them into consolidated images that take up less disk space. The amount of memory needed to store such photos has been lowered. The amount of memory is determined by the number of modalities fused.

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33

Htet, Aung Si Min, and Hyo Jong Lee. "Contactless Palm Vein Recognition Based on Attention-Gated Residual U-Net and ECA-ResNet." Applied Sciences 13, no. 11 (May 23, 2023): 6363. http://dx.doi.org/10.3390/app13116363.

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Palm vein recognition has received some considerable attention regarding its use in biometric identification. Palm vein characteristics offer a superior level of security and reliability in personal identification compared to extrinsic methods such as fingerprint, face, and palm print recognition, as vein patterns are difficult to duplicate and do not change throughout one’s lifetime. This study proposes both segmentation and recognition methods to enhance the authentication performance and achieve correct identification using palm vein features. First, we propose a segmentation model based on the U-Net model, enhanced with an attention gate, to effectively segment palm vein patterns. The incorporation of both the attention gate and residual block allows the segmentation model for the learning of the essential features required for specific segmentation tasks. The Hessian-based Jerman filtering method is used for ground-truth labeling. The segmentation model extracts the palm vein patterns and filters out the irrelevant and noisy pixels for the purpose of recognition. The efficient channel attention residual network is trained to learn discriminative features for personal identification using combined margin-based loss functions for palm vein recognition. The channel attention module enhances the useful information and suppresses irrelevant features in the feature maps, which overcomes the problem of rotation, position translation, and scale transformation, as well as improves the recognition rate. The combined loss function used in this study increases the similarity between the intra-class samples and the diversity between inter-class samples. The proposed recognition model achieved 100% accuracy for palm vein recognition and an equal error rate of 0.018 for palm vein verification.

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Kanchana, A., and S. Arumugam. "Biometric Palm Print Recognition using Spatial Classifiers and Morphological Texture Segmentation." International Journal of Computer Applications 64, no. 19 (February 15, 2013): 1–4. http://dx.doi.org/10.5120/10739-4974.

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35

Chaudhari, Jitendra P. "Supervised Feature Reduction Technique for Biometric Recognition Using Palm print Modalities." Bioscience Biotechnology Research Communications 13, no. 1 (March 25, 2020): 195–200. http://dx.doi.org/10.21786/bbrc/13.1/34.

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36

Kureel, Srushti, and Praveen Kumar. "Shape and Texture based Palm Print Recognition System for Biometric identification." International Journal of Engineering Trends and Technology 50, no. 1 (August 25, 2017): 39–44. http://dx.doi.org/10.14445/22315381/ijett-v50p207.

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37

Imtiaz, Hafiz, and Shaikh Anowarul Fattah. "A wavelet-based dominant feature extraction algorithm for palm-print recognition." Digital Signal Processing 23, no. 1 (January 2013): 244–58. http://dx.doi.org/10.1016/j.dsp.2012.06.016.

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38

Naveena, C., Shreyas Rangappa, and Chethan H. K. "Texture Features in Palmprint Recognition System." International Journal of Natural Computing Research 10, no. 1 (January 2021): 41–57. http://dx.doi.org/10.4018/ijncr.2021010104.

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This paper describes the algorithm used for personal identification based on features extracted from the palmprint. The local Gabor XOR (LGXP) features is built using Gabor filter with orientation. Initially, the palm print images are preprocessed using median filter. The algorithm is then modified, where features are extracted with different orientations of the Gabor filter called the multiple orientation LGXP (MOLGXP) features. The PCA feature is extracted and fused with MOLGXP and PCA using sum rule.

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39

Kumar, H. Kishore, and S. Ashok Kumar. "Accurate Biometric Palm Print Recognition Using ResNet50 algorithm Over X Gradient Boosting Algorithm." E3S Web of Conferences 399 (2023): 04027. http://dx.doi.org/10.1051/e3sconf/202339904027.

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The aim of this research is to enhance the accuracy of biometric palm print identification by using the Novel ResNet50 Algorithm as compared to the X Gradient Boosting. Materials and Methods: In this study, the ResNet50 and X Gradient Boosting algorithms were compared using a sample size of 10 for each algorithm, resulting in a total sample size of 20. The comparison was carried out with a G Power of 0.8 and a confidence interval (CI) of 95% to ensure statistical significance. For this study the Birjand University Mobile Palmprint Database (BMPD) dataset was collected from the Kaggle repository, which includes a total of 1640 images containing both left and right-hand palmprints. Result: According to the results, the ResNet50 algorithm achieved a higher accuracy rate (94.7%) compared to the X Gradient Boosting algorithm (92.4%) in identifying and measuring the images. The statistical analysis indicated a significant difference between the Novel ResNet50 algorithm and X Gradient Boosting, with a pvalue of 0.003 (Independent sample T-test p<0.05). This suggests that the ResNet50 algorithm outperformed the X Gradient Boosting algorithm in this experiment. According to the study’s findings, ResNet50 is more effective in accurately identifying biometric palm prints compared to X Gradient Boosting.

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40

Walid, Hosne-Al, Tasnia Sadia, Anika Tahsin, Tanzima Asad, and Nishat Tasnim. "Palm Print Recognition System using Naive Bayes Classifier and Image Processing Tools." Communications on Applied Electronics 2, no. 6 (August 25, 2015): 45–49. http://dx.doi.org/10.5120/cae2015651794.

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41

Bala, Shashi, and Nidhi. "Comparative Analysis of Palm Print Recognition System with Repeated Line Tracking Method." Procedia Computer Science 92 (2016): 578–82. http://dx.doi.org/10.1016/j.procs.2016.07.385.

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Imtiaz, Hafiz, and Shaikh Anowarul Fattah. "A DCT-Based Local Dominant Feature Extraction Algorithm for Palm-Print Recognition." Circuits, Systems, and Signal Processing 32, no. 3 (October 16, 2012): 1179–204. http://dx.doi.org/10.1007/s00034-012-9493-z.

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Chaudhari, Jitendra, Hiren Mewada, Amit Patel, Keyur Mahant, and Alpesh Vala. "COMPUTATIONLESS PALM-PRINT VERIFICATION USING WAVELET ORIENTED ZERO-CROSSING SIGNATURE." IIUM Engineering Journal 23, no. 1 (January 4, 2022): 222–32. http://dx.doi.org/10.31436/iiumej.v23i1.2086.

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Palmprints can be characterized by their texture and the patterns of that texture dominate in a vertical direction. Therefore, the energy of the coefficients in the transform domain is more concentrated in the vertical sideband. Using this idea, this paper proposes the characterization of the texture features of the palmprint using zero-crossing signatures based on the dyadic discrete wavelet transform (DWT) to effectively identify an individual. A zero-crossing signature of 4 x 256 was generated from the lower four resolution levels of dyadic DWT in the enrolment process and stored in the database to identify the person in recognition mode. Euclidean distance was determined to find the best fit for query palmprints zero-crossing signature from the dataset. The proposed algorithm was tested on the PolyU dataset containing 6000 multi-spectral images. The proposed algorithm achieved 96.27% accuracy with a lower recognition time of 0.76 seconds. ABSTRAK: Pengesan Tapak Tangan boleh dikategorikan berdasarkan ciri-ciri tekstur dan corak pada tekstur yang didominasi pada garis tegak. Oleh itu, pekali tenaga di kawasan transformasi adalah lebih penuh pada jalur-sisi menegak. Berdasarkan idea ini, cadangan kajian ini adalah berdasarkan ciri-ciri tekstur pada tapak tangan dan tanda pengenalan sifar-silang melalui transformasi gelombang kecil diadik yang diskret (DWT) bagi mengecam individu. Pada mod pengecaman, tanda pengenalan sifar-silang 4 x 256 yang terhasil daripada tahap diadik resolusi empat terendah DWT digunakan dalam proses kemasukan dan simpanan di pangkalan data bagi mengenal pasti individu. Jarak Euklidan yang terhasil turut digunakan bagi memperoleh padanan tapak tangan paling sesuai melalui tanda pengenalan sifar-silang dari set data. Algoritma yang dicadangkan ini diuji pada set data PolyU yang mengandungi 6000 imej pelbagai-spektrum. Algoritma yang dicadangkan ini berjaya mencapai ketepatan sebanyak 96.27% dengan durasi pengecaman berkurang sebanyak 0.76 saat.

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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 (February 27, 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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T., Vijayakumar. "Synthesis of Palm Print in Feature Fusion Techniques for Multimodal Biometric Recognition System Online Signature." Journal of Innovative Image Processing 3, no. 2 (July 1, 2021): 131–43. http://dx.doi.org/10.36548/jiip.2021.2.005.

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Biometric identification technology is widely utilized in our everyday lives as a result of the rising need for information security and safety laws throughout the world. In this aspect, multimodal biometric recognition (MBR) has gained significant research attention due to its ability to overcome several important constraints in unimodal biometric systems. Henceforth, this research article utilizes multiple features such as an iris, face, finger vein, and palm print for obtaining the highest accuracy to identify the exact person. The utilization of multiple features from the person improves the accuracy of biometric system. In many developed countries, palm print features are employed to provide the most accurate identification of an actual individual as fast as possible. The proposed system can be very suitable for the person who dislikes answering many questions for security authentication. Moreover, the proposed system can also be used to minimize the extra questionnaire by achieving a highest accuracy than other existing multimodal biometric systems. Finally, the results are computed and tabulated in this research article.

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Vinothkanna, R., and Amitabh Wahi. "A Multimodal Biometric Approach for the Recognition of Finger Print, Palm Print and Hand Vein using Fuzzy Vault." International Journal of Biomedical Engineering and Technology 1, no. 1 (2017): 1. http://dx.doi.org/10.1504/ijbet.2017.10017685.

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Vinothkanna, R., and Amitabh Wahi. "A multimodal biometric approach for the recognition of finger print, palm print and hand vein using fuzzy vault." International Journal of Biomedical Engineering and Technology 33, no. 1 (2020): 54. http://dx.doi.org/10.1504/ijbet.2020.107650.

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Imtiaz, Hafiz, and Shaikh Anowarul Fattah. "A histogram-based dominant wavelet domain feature selection algorithm for palm-print recognition." Computers & Electrical Engineering 39, no. 4 (May 2013): 1114–28. http://dx.doi.org/10.1016/j.compeleceng.2013.01.006.

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Arunachalamand, MuthuKumar, and Kavipriya Amuthan. "Finger Knuckle Print Recognition using MMDA with Fuzzy Vault." International Arab Journal of Information Technology 17, no. 4 (July 1, 2020): 554–61. http://dx.doi.org/10.34028/iajit/17/4/14.

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Currently frequent biometric scientific research such as with biometric applications like face, iris, voice, hand-based biometrics traits like palm print and fingerprint technique are utilized for spotting out the persons. These specific biometrics habits have their own improvement and weakness so that no particular biometrics can adequately opt for all terms like the accuracy and cost of all applications. In recent times, in addition, to distinct with the hand-based biometrics technique, Finger Knuckle Print (FKP) has been appealed to boom the attention among biometric researchers. The image template pattern formation of FKP embraces the report that is suitable for spotting the uniqueness of individuality. This FKP trait observes a person based on the knuckle print and the framework in the outer finger surface. This FKP feature determines the line anatomy and finger structures which are well established and persistent throughout the life of an individual. In this paper, a novel method for personal identification will be introduced, along with that data to be stored in a secure way has also been proposed. The authentication process includes the transformation of features using 2D Log Gabor filter and Eigen value representation of Multi-Manifold Discriminant Analysis (MMDA) of FKP. Finally, these features are grouped using k-means clustering for both identification and verification process. This proposed system is initialized based on the FKP framework without a template based on the fuzzy vault. The key idea of fuzzy vault storing is utilized to safeguard the secret key in the existence of random numbers as chaff pints

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Xianping WU. "Research on the Palm Print Recognition Algorithm Research Based on Improved Geometric Invariance Principle." INTERNATIONAL JOURNAL ON Advances in Information Sciences and Service Sciences 4, no. 3 (February 29, 2012): 97–103. http://dx.doi.org/10.4156/aiss.vol4.issue3.13.

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