Bibliographies thématiques / PALM PRINT RECOGNITION

Littérature scientifique sur le sujet « PALM PRINT RECOGNITION »

Auteur : Grafiati
Publié le 11 septembre 2023

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Articles de revues sur le sujet "PALM PRINT RECOGNITION"

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Goh, Michael K. O., Connie Tee et Andrew B. J. Teoh. « BI-MODAL PALM PRINT AND KNUCKLE PRINT RECOGNITION SYSTEM ». Journal of IT in Asia 3, no 1 (20 avril 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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AlShemmary, Ebtesam. « Siamese Network-Based Palm Print Recognition ». Journal of Kufa for Mathematics and Computer 10, no 1 (31 mars 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., et N. B. Prajwala. « A Scientific Analysis to Observe Uniqueness in Lip Print Pattern ». International Journal of Innovative Technology and Exploring Engineering 10, no 4 (28 février 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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Karar, Subhajit, et Ranjan Parekh. « Palm Print Recognition using Zernike Moments ». International Journal of Computer Applications 55, no 16 (20 octobre 2012) : 15–19. http://dx.doi.org/10.5120/8839-3069.

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

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

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ATTAR SHAGUSTHA BANU et 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 (28 février 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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Mustafa, Raniah Ali, Haitham Salman Chyad et Rafid Aedan Haleot. « Palm print recognition based on harmony search algorithm ». International Journal of Electrical and Computer Engineering (IJECE) 11, no 5 (1 octobre 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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Jaafar, Haryati, Salwani Ibrahim et 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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Michael, Goh Kah Ong, Tee Connie et Andrew Teoh Beng Jin. « An innovative contactless palm print and knuckle print recognition system ». Pattern Recognition Letters 31, no 12 (septembre 2010) : 1708–19. http://dx.doi.org/10.1016/j.patrec.2010.05.021.

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Thèses sur le sujet "PALM PRINT RECOGNITION"

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GUPTA, AMIT. « IMPROVED PALM PRINT RECOGNITION ». Thesis, 2016. http://dspace.dtu.ac.in:8080/jspui/handle/repository/15794.

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In existing biometric framework finger prints are normally forged; voice, signature, and hand shapes are easily forged; and biometrics, for example, fingerprints, iris and face recognition, are susceptible to forge, i.e., the biometric identifiers can be duplicated and used to make artifacts that can betray numerous presently accessible biometric gadgets. The biggest challenge to bio-metrics is to improve recognition performance in terms of accuracy and efficiency both and become maximum resistant to spoofing attacks. To this end, numerous specialists have tried to enhance unwavering quality and baffle spoofs by creating biometrics that are exceedingly individuating. Thus in this respect Palm print Pattern recognition has been introduced which is highly complex, hopefully insuperable challenge to those who wish to defeat them because blood vessels are hidden inside the body and furthermore in this there is no physical contact between the client and system. In the present research work efforts are made to propose a system which can improve the performance in terms of accuracy of the palm print patterns and their features, which is recognized from the given images, a public database of palm prints “SDUMLA-HMT”. The proposed work includes the Image Processing using MATLAB R2014a and several other algorithms.
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Li, Tzung-Ru, et 李宗儒. « Palm-Print Recognition Based on Principal Line Features using Hough Palm-Print Recognition Based on Principal Line Features using Hough Transform ». Thesis, 2008. http://ndltd.ncl.edu.tw/handle/03587807640224762650.

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碩士
國立暨南國際大學
通訊工程研究所
96
With an increasing emphasis on security, personal authentication based on biometrics has been receiving extensive attention over the past decade. Among many different biometric technologies, this thesis examines palm-print technique for personal identification and develops a good performance recognition system based on human hand features. It is implemented and tested on VIP-CC Lab. hand image database. The proposed system includes four modules: image acquisition, image pre-processing, feature extraction, and recognition modules. The system captures a hand image using digital camera, then uses some image processing algorithms to localize the region of the interest of palmprint from the hand image via image pre-processing module. The feature extraction module segments the region of interest image, and adopts its discriminating texture features calculated by Hough transform form each block. The system applies these feature vectors for matching in recognition module. Experimental results show that the system has an encouraging performance on the VIP-CC Lab. Database (Including 678 images from 113 classes) . The proposed system adopts Hough transform to extract principal line feature of palmprint. When we separate palmprint image into 8×8 blocks, adopting principal line segment length as our features in each block can attain an equal error rate (EER) of 3.55%. When we separate palmprint image into 10×10 blocks, and use binary coding to generate the feature codes that we can attain an EER of 1.9071%. This thesis analyzes the experimented results and verifies the related inferences of the proposed system for providing useful information for further research.
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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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Chiang, Yao-Shan, et 江樂山. « Palm-Print and Hand-Shape Biometric Recognition System Based on Wavelet Transform and Statistical Moments ». Thesis, 2005. http://ndltd.ncl.edu.tw/handle/65358393795776210022.

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碩士
國立暨南國際大學
電機工程學系
93
With an increasing emphasis on security, personal authentication based on biometrics has been receiving extensive attention over the past decade. Among many different biometric technologies, this thesis examines palm-print and hand-shape technique for personal identification and develops a good performance recognition system based on human hand features. It is implemented and tested on VIP-CC Lab. hand image database. The proposed system includes four modules: image acquisition, image pre-processing, feature extraction, and recognition modules. First, the system captures a hand image using digital camera, then uses some image processing algorithms to localize the region of the interest of palm-print and hand-geometry from the hand image via image pre-processing module. The feature extraction module adopts the gradient direction （i.e., angle） of the two different wavelet transforms in the palm-print phase, and adopts the statistical moments in the hand-shape to extract the discriminating texture features. The system encodes the feature to generate its palm-print codes by binary gray coding, and uses invariant moment vector in hand-geometry phase. Finally, the system applies these feature codes and vector for matching in recognition module. Experimental results show that the system has an encouraging performance on the VIP-CC Lab. database（including 210 images from 30 classes）. The proposed system adopts two different wavelet transform and statistical moments to extract palm-print and hand-shape features, then uses the gradient direction coding to generate the feature codes. We attain the recognition rates up to 95.00% and 98.33%（according to equal error rate, EER）, respectively. Even under the circumstance of false acceptance rate（FAR） 0%, the system still approaches the recognition rate above 89.17%（acceptance of authentic, AA）. This thesis analyzes the experimented results and verifies the related inferences of the proposed system for providing useful information for further research.
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Chapitres de livres sur le sujet "PALM PRINT RECOGNITION"

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Poonia, Poonam, et Pawan K. Ajmera. « Robust Multi-Spectral Palm-Print Recognition ». Dans Lecture Notes in Networks and Systems, 285–93. Singapore : Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-0483-9_25.

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Mod, Mayank, Amit Mishra, Kusha Bhatt, Sonal Shah, Shivali Shah et Urvashi Sanadhya. « An Exploration of Miscellaneous Palm Print Recognition Modalities ». Dans Communications in Computer and Information Science, 69–76. Singapore : Springer Singapore, 2016. http://dx.doi.org/10.1007/978-981-10-3433-6_9.

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Rane, Milind, et Umesh Bhadade. « Dual Palm Print-Based Human Recognition Using Fusion ». Dans Algorithms for Intelligent Systems, 101–9. Singapore : Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-0873-5_9.

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Badrinath, G. S., et Phalguni Gupta. « A Novel Representation of Palm-Print for Recognition ». Dans Computer Vision – ACCV 2010, 321–33. Berlin, Heidelberg : Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-19309-5_25.

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Han, Chin-Chuan. « Personal Authentication Using the Fusion of Multiple Palm-Print Features ». Dans Computer-Aided Intelligent Recognition Techniques and Applications, 131–43. Chichester, UK : John Wiley & Sons, Ltd, 2005. http://dx.doi.org/10.1002/0470094168.ch9.

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Manoj, M. Sowmiya, et S. Arulselvi. « Artificial Neural Network Based Biometric Palm Print Recognition System for Security Analysis ». Dans Lecture Notes in Electrical Engineering, 808–19. Singapore : Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-1677-9_70.

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Dai, Gui-Ping. « Palm Print Feature Extraction and Recognition Based on BEMD-ICAII and LS-SVM ». Dans Intelligent Computing Theories, 368–75. Berlin, Heidelberg : Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-39479-9_44.

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Dastidar, Jayati Ghosh, Debangshu Chakraborty, Soumen Mukherjee et Arup Kumar Bhattacharjee. « Analysis of Human Gait for Designing a Recognition and Classification System ». Dans Intelligent Innovations in Multimedia Data Engineering and Management, 186–200. IGI Global, 2019. http://dx.doi.org/10.4018/978-1-5225-7107-0.ch008.

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Identification and recognition of a human subject by monitoring a video/image by using various biometric features such as fingerprints, retina/iris scans, palm prints have been of interest to researches. In this chapter, an attempt has been made to recognize a human subject uniquely by monitoring his/her gait. This has been done by analyzing sampled frames of a video sequence to first detect the presence of a human form and then extract the silhouette of the subject in question. The extracted silhouette is then used to find the skeleton from it. The skeleton contains a set of points that retains the connectivity of the form and maintains the geometric properties of the silhouette. From the skeleton, a novel method has been proposed involving the neighborhood of interest pixels to identify the end points representing the heel, toe, etc. These points finally lead to the calculation of gait attributes. The extracted attributes represented in the form of a pattern vector are matched using cosine distance with features stored in the database resulting in identification/rejection.
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Actes de conférences sur le sujet "PALM PRINT RECOGNITION"

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Harb, Ahmad, Mahmoud Abbas, Ali Cherry, Hussein Jaber et Mohamad Ayache. « Palm print recognition ». Dans 2015 International Conference on Advances in Biomedical Engineering (ICABME). IEEE, 2015. http://dx.doi.org/10.1109/icabme.2015.7323239.

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Agarwal, Shalini, Vivek Sharma et Pawan Kumar Verma. « Palm Print Recognition Using CEDA ». Dans 2019 3rd International Conference on Computing Methodologies and Communication (ICCMC). IEEE, 2019. http://dx.doi.org/10.1109/iccmc.2019.8819834.

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Kaushik, Shivkant, et Rajendra Singh. « A new hybrid approch for palm print recognition in PCA based palm print recognition system ». Dans 2016 5th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO). IEEE, 2016. http://dx.doi.org/10.1109/icrito.2016.7784958.

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Ray, Kasturika B., et Rachita Misra. « Palm Print Recognition Using Hough Transforms ». Dans 2015 International Conference on Computational Intelligence and Communication Networks (CICN). IEEE, 2015. http://dx.doi.org/10.1109/cicn.2015.88.

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Rajawat, A., M. Hanmandlu et S. Pani. « Fuzzy modeling based palm print recognition system ». Dans 2009 International Conference on Emerging Trends in Electronic and Photonic Devices & Systems (ELECTRO-2009). IEEE, 2009. http://dx.doi.org/10.1109/electro.2009.5441141.

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Swarnkar, Priyanshi, et P. K. Jain. « Palm print Recognition Using Neighboring Direction Indicator ». Dans 2019 Sixteenth International Conference on Wireless and Optical Communication Networks (WOCN). IEEE, 2019. http://dx.doi.org/10.1109/wocn45266.2019.8995053.

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Javidnia, Hossein, Adrian Ungureanu et Peter Corcoran. « Palm-print recognition for authentication on smartphones ». Dans 2015 IEEE International Symposium on Technology and Society (ISTAS). IEEE, 2015. http://dx.doi.org/10.1109/istas.2015.7439441.

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Aishwarya, D., M. Gowri et R. K. Saranya. « Palm print recognition using liveness detection technique ». Dans 2016 Second International Conference on Science Technology Engineering And Management (ICONSTEM). IEEE, 2016. http://dx.doi.org/10.1109/iconstem.2016.7560933.

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Yang, Jun, et Xianhong Zhao. « Palm print image processing with PCNN ». Dans International Conference on Image Processing and Pattern Recognition in Industrial Engineering, sous la direction de Zhengyu Du et Bin Liu. SPIE, 2010. http://dx.doi.org/10.1117/12.867026.

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Cui, Yuan, et Bo-nian Li. « A Palm-Print Recognition System Based on OMAP3530 ». Dans 2010 6th International Conference on Wireless Communications, Networking and Mobile Computing (WiCOM). IEEE, 2010. http://dx.doi.org/10.1109/wicom.2010.5600686.

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Rapports d'organisations sur le sujet "PALM PRINT RECOGNITION"

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Varastehpour, Soheil, Hamid Sharifzadeh, Iman Ardekani et Abdolhossein Sarrafzadeh. Human Biometric Traits : A Systematic Review Focusing on Vascular Patterns. Unitec ePress, décembre 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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