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Relevant bibliographies by topics / PALM PRINT RECOGNITION

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Journal articles

Academic literature on the topic 'PALM PRINT RECOGNITION'

Author: Grafiati

Published: 11 September 2023

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Journal articles on the topic "PALM PRINT RECOGNITION"

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 (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 (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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3

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 (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 (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 (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 (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 (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 (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 (2010): 1708–19. http://dx.doi.org/10.1016/j.patrec.2010.05.021.

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