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Journal articles on the topic 'Knuckle prints'

Author: Grafiati

Published: 7 June 2025

Last updated: 2 August 2025

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1

Ardra, Vijay V. R.*. "Comparative and Identify the Kunckle Prints in Right and Left Hand of Male and Female, On the Basis of Bifurcation and Ridge Ending." International Journal of Scientific Research and Technology 2, no. 5 (2025): 117–23. https://doi.org/10.5281/zenodo.15345253.

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Knuckle prints are unique patterns, it contains ridges and furrows on the dorsal surface of the hand. Knuckle prints are used biometric identifier. These prints contain biological features like minutiae.The objective of this research is to investigate, the comparative analysis of knuckle prints in both hands of male and female individual. The study emphasizes the variations in ridge endings and bifurcations between right and left hand. Data collection involves gathering knuckle patterns from individuals aged 20 and above, with a target of 100 samples from each gender. The analysis focuses on t

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2

Hegde, Chetana, P. Deepa Shenoy, K. R. Venugopal, and L. M. Patnaik. "Authentication using Finger Knuckle Prints." Signal, Image and Video Processing 7, no. 4 (2013): 633–45. http://dx.doi.org/10.1007/s11760-013-0469-7.

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3

Biswas, Prachi Sharma, and Swati Dubey Mishra. "Novel Approach For Personal Identification Using Dorsal Knuckle Crease Patterns: A Pilot Study." Mapana Journal of Sciences 23, no. 4 (2024): 83–97. https://doi.org/10.12723/mjs.71.5.

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Despite the availability of various identification methods, such as fingerprints, ridge density, palm prints, and vein patterns, forensic identification remains a complex challenge. While substantial research has been conducted on the palmar surface of the hand, there has been limited focus on the dorsal surface for identification purposes. The dorsal surface, like fingerprints, contains minutiae and skin crease patterns believed to be permanent. The crease patterns present on the dorsal side of the proximal interphalangeal joints are known as knuckle crease patterns. This research aimed to cl

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Grover, J., and M. Hanmandlu. "Personal identification using the rank level fusion of finger-knuckle-prints." Pattern Recognition and Image Analysis 27, no. 1 (2017): 82–93. http://dx.doi.org/10.1134/s1054661817010059.

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Maheshwari, Uma, and Kalpana Kalpana. "Multimodal Image Fusion in Biometric Authentication." Fusion: Practice and Applications 1, no. 2 (2020): 79–91. http://dx.doi.org/10.54216/fpa.010203.

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During this study, a unique multimodal biometric system was constructed. This system incorporated a variety of unimodal biometric inputs, including fingerprints, palmprints, knuckle prints, and retina images. The multimodal system generated the fused template via feature-level fusion, which combined several different biometric characteristics. The Gabor filter extracted the features from the various biometric aspects. The fusion of the extracted information from the fingerprint, knuckle print, palmprint, and retina into a single template, which was then saved in the database for authentication

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6

Eaker, Adam. "The Art of Marring a Face: Exhibiting Boxers in Georgian London." Huntington Library Quarterly 87, no. 2 (2024): 165–82. https://doi.org/10.1353/hlq.2024.a964270.

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ABSTRACT: Bare-knuckle boxing matches provided some of the most ambivalent experiences of spectatorship in late Georgian England. Brutally violent and banned by law, prizefights nonetheless enjoyed an ardent following. Artists attended bouts, reveling in the opportunity to study musclebound athletes who seemed to reincarnate classical perfection. Pugilists in turn borrowed the language of artistic display, terming their training grounds “academies” and their bouts “exhibitions.” They also inspired a proliferation of images, ranging from grand painted portraits to popular prints that both celeb

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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

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8

Yadav, Vandana, Vinayak Bharadi, and Sunil K. Yadav. "Texture Feature Extraction Using Hybrid Wavelet Type I & II for Finger Knuckle Prints for Multi-algorithmic Feature Fusion." Procedia Computer Science 79 (2016): 359–66. http://dx.doi.org/10.1016/j.procs.2016.03.047.

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9

Yadav, Vandana, Vinayak Bharadi, and Sunil K. Yadav. "Feature Vector Extraction based Texture Feature using Hybrid Wavelet Type I & II for Finger Knuckle Prints for Multi-Instance Feature Fusion." Procedia Computer Science 79 (2016): 351–58. http://dx.doi.org/10.1016/j.procs.2016.03.046.

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10

Abdullahi, Umar, and Hambali Moshood Abiola. "Survey of Finger Knuckle Print Recognition and Authentication." Kwaghe International Journal of Sciences and Technology 1, no. 1 (2024): 383–402. https://doi.org/10.58578/kijst.v1i1.3611.

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Background: Finger knuckle (FK) has gained significant attention as a biometric characteristic in recent years. Its unique features, such as visible lines, wrinkles, and ridges on the external surface of finger knuckles, make it an economically viable option for human identification. FK serves as the foundation for many biometric systems. Aim: This report presents a comprehensive analysis of relevant FK research. The typical FK identification system consists of four steps: image acquisition, image preprocessing, feature extraction, and matching. Various methods have been employed at each stage

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11

Chalabi, Nour Elhouda, Abdelouahab Attia, and Abderraouf Bouziane. "MULTIMODAL FINGER DORSAL KNUCKLE MAJOR AND MINOR PRINT RECOGNITION SYSTEM BASED ON PCANET DEEP LEARNING." ICTACT Journal on Image and Video Processing 10, no. 3 (2020): 2153–58. https://doi.org/10.21917/ijivp.2020.0308.

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Hand-based recognition systems with different traits are widely used techniques and are trustworthy ones. We can find it in different real life fields such as banks and industries due to its stability, reliability, acceptability, and the wide range features. In this paper, we present a finger dorsal knuckle print multimodal recognition system, where we use PCAnet (principal component analysis network) deep learning to extract the features from both Major and Minor finger dorsal knuckles to allow a deeper insight into the exploited trait. Then SVM is used for the matching stage of the two modal

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12

Neware, Shubhangi, Kamal Mehta, and A. S. Zadgaonkar. "Finger Knuckle Print Identification using Gabor Features." International Journal of Computer Applications 98, no. 18 (2014): 22–24. http://dx.doi.org/10.5120/17283-7641.

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13

Hegde, Dhananjay Kumar, and Priyanka Gupta. "A study on identification of knuckle print." International Journal of Forensic Medicine 6, no. 1 (2024): 41–48. http://dx.doi.org/10.33545/27074447.2024.v6.i1a.79.

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14

Veluchamy, S., and L. R. Karlmarx. "HE-Co-HOG and k-SVM classifier for finger knuckle and palm print-based multimodal biometric recognition." Sensor Review 40, no. 2 (2020): 203–16. http://dx.doi.org/10.1108/sr-09-2017-0203.

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Purpose Biometric identification system has become emerging research field because of its wide applications in the fields of security. This study (multimodal system) aims to find more applications than the unimodal system because of their high user acceptance value, better recognition accuracy and low-cost sensors. The biometric identification using the finger knuckle and the palmprint finds more application than other features because of its unique features. Design/methodology/approach The proposed model performs the user authentication through the extracted features from both the palmprint a

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15

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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16

SHARIATMADAR, ZAHRA S., and KARIM FAEZ. "FINGER-KNUCKLE-PRINT RECOGNITION VIA ENCODING LOCAL-BINARY-PATTERN." Journal of Circuits, Systems and Computers 22, no. 06 (2013): 1350050. http://dx.doi.org/10.1142/s0218126613500503.

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Biometrics-based authentication is an effective approach which is used for automatically recognizing a person's identity. Recently, it has been found that the finger-knuckle-print (FKP), which refers to the texture pattern produced by the finger knuckle bending, is highly unique and can be used as a biometric identifier. In this paper, we present an effective FKP recognition scheme for personal identification and identity verification. This method is a new encoding scheme based on local binary pattern (LBP). Each image first is decomposed in several blocks, each block is convolved with a bank

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17

Kim, Min-Ki. "Finger-Knuckle Print Recognition Using Gradient Orientation Feature." Journal of the Korea Contents Association 12, no. 12 (2012): 517–23. http://dx.doi.org/10.5392/jkca.2012.12.12.517.

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18

Tarawneh, Ahmad S., Ahmad B. Hassanat, Esra’a Alkafaween, et al. "DeepKnuckle: Deep Learning for Finger Knuckle Print Recognition." Electronics 11, no. 4 (2022): 513. http://dx.doi.org/10.3390/electronics11040513.

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Biometric technology has received a lot of attention in recent years. One of the most prevalent biometric traits is the finger-knuckle print (FKP). Because the dorsal region of the finger is not exposed to surfaces, FKP would be a dependable and trustworthy biometric. We provide an FKP framework that uses the VGG-19 deep learning model to extract deep features from FKP images in this paper. The deep features are collected from the VGG-19 model’s fully connected layer 6 (F6) and fully connected layer 7 (F7). After applying multiple preprocessing steps, such as combining features from different

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19

Zhang, Lin, Lei Zhang, David Zhang, and Hailong Zhu. "Online finger-knuckle-print verification for personal authentication." Pattern Recognition 43, no. 7 (2010): 2560–71. http://dx.doi.org/10.1016/j.patcog.2010.01.020.

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20

Liu, Ming, Yongmei Tian, and Li Lihua. "A new approach for inner-knuckle-print recognition." Journal of Visual Languages & Computing 25, no. 1 (2014): 33–42. http://dx.doi.org/10.1016/j.jvlc.2013.10.003.

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21

Brindha, V. Evelyn. "Finger Knuckle Print as Unimodal Fuzzy Vault Implementation." Procedia Computer Science 47 (2015): 205–13. http://dx.doi.org/10.1016/j.procs.2015.03.199.

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22

Surameery, Nigar M. Shafiq. "Deep ContactlessS 3D-Middle Finger Knuckle Recognition." Science Journal of University of Zakho 9, no. 4 (2021): 171–77. http://dx.doi.org/10.25271/sjuoz.2021.9.4.854.

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Automation security is one of the main concerns of modern times. A safe and reliable identity verification system is in great demand. A biometric verification system can represent a reliable method of identifying an individual. The knuckle pattern is considered to be one of the emerging hand biometrics because it has the potential to identify individuals. This presented work explores the possibility of using 3D middle finger knuckle for biometric identification. The study provides a new simple, trained from scratch but effective deep convolutional neural network model that designed for 3D figu

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23

Li, Wenwen. "Biometric Recognition of Finger Knuckle Print Based on the Fusion of Global Features and Local Features." Journal of Healthcare Engineering 2022 (January 7, 2022): 1–11. http://dx.doi.org/10.1155/2022/6041828.

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Compared with the most traditional fingerprint identification, knuckle print and hand shape are more stable, not easy to abrase, forge, and pilfer; in aspect of image acquisition, the requirement of acquisition equipment and environment are not high; and the noncontact acquisition method also greatly improves the users’ satisfaction; therefore, finger knuckle print and hand shape of single-mode identification system have attracted extensive attention both at home and abroad. A large number of studies show that multibiometric fusion can greatly improve the recognition rate, antiattack, and robu

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24

Yang, Wen Ming, Yi Chao Li, and Qing Min Liao. "Fast and Robust Personal Identification by Fusion of Finger Vein and Finger-Knuckle-Print Images." Applied Mechanics and Materials 556-562 (May 2014): 5085–88. http://dx.doi.org/10.4028/www.scientific.net/amm.556-562.5085.

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Finger vein and finger-knuckle-print have been studied for personal identification. Methods utilizing direction and location of finger veins have achieved promising performance. However, it is sensitive to quality of finger vein images and it is slow. In this paper, we develop a fast and robust algorithm for person recognition using a coarse-to-fine classifier. 2DPCA is used for coarse selection of k nearest candidates. To increase the robustness of the algorithm, a candidate person is selected when either its finger vein or finger-knuckle-print is near the corresponding test sample. Competiti

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25

Neware, Shubhangi. "Multimodal Biometric Using Fusion of Fingerprint, Finger Knuckle Print and Palm Print." Bioscience Biotechnology Research Communications 13, no. 14 (2020): 62–65. http://dx.doi.org/10.21786/bbrc/13.14/15.

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26

Aljuboori, Ali Mohsin, and Mohammed Hamzah Abed. "Finger knuckle pattern person identification system based on LDP-NPE and machine learning methods." Bulletin of Electrical Engineering and Informatics 11, no. 6 (2022): 3521–29. http://dx.doi.org/10.11591/eei.v11i6.4236.

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Biometric-based individual distinguishing proof is a successful strategy for consequently perceiving, with high certainty, an individual's character. The utilization of finger knuckle pictures for individual ID has shown promising outcomes and produced a ton of interest in biometrics. By seeing that the surface example delivered by twisting the finger knuckle is profoundly particular, in this paper we present a new biometric validation framework utilizing finger-knuckle-print (FKP) imaging. In this paper, another methodology in view of neighborhood surface examples is proposed. Local derivativ

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27

Ali, Mohsin Aljuboori, and Hamzah Abed Mohammed. "Finger knuckle pattern person identification system based on LDP-NPE and machine learning methods." Bulletin of Electrical Engineering and Informatics 11, no. 6 (2022): 3521~3529. https://doi.org/10.11591/eei.v11i6.4236.

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Biometric-based individual distinguishing proof is a successful strategy for consequently perceiving, with high certainty, an individual's character. The utilization of finger knuckle pictures for individual ID has shown promising outcomes and produced a ton of interest in biometrics. By seeing that the surface example delivered by twisting the finger knuckle is profoundly particular, in this paper we present a new biometric validation framework utilizing finger-knuckle-print (FKP) imaging. In this paper, another methodology in view of neighborhood surface examples is proposed. Local deriv

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28

Arunachalamand, MuthuKumar, and Kavipriya Amuthan. "Finger Knuckle Print Recognition using MMDA with Fuzzy Vault." International Arab Journal of Information Technology 17, no. 4 (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

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29

Sathiya, L., and V. Palanisamy. "A Survey on Finger Knuckle Print based Biometric Authentication." International Journal of Computer Sciences and Engineering 6, no. 8 (2018): 236–40. http://dx.doi.org/10.26438/ijcse/v6i8.236240.

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30

Malik, Jyoti, Ratna Dahiya, Dhiraj Girdhar, and G. Sainarayanan. "Finger knuckle print authentication using Canny edge detection method." International Journal of Signal and Imaging Systems Engineering 9, no. 6 (2016): 333. http://dx.doi.org/10.1504/ijsise.2016.080267.

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31

Sainarayanan, G., Dhiraj Girdhar, Jyoti Malik, and Ratna Dahiya. "Finger knuckle print authentication using Canny edge detection method." International Journal of Signal and Imaging Systems Engineering 9, no. 6 (2016): 333. http://dx.doi.org/10.1504/ijsise.2016.10000770.

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32

Yang, Wankou, Changyin Sun, and Zhenyu Wang. "Finger-knuckle-print recognition using Gabor feature and MMDA." Frontiers of Electrical and Electronic Engineering in China 6, no. 2 (2011): 374–80. http://dx.doi.org/10.1007/s11460-011-0141-3.

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33

Morales, A., C. M. Travieso, M. A. Ferrer, and J. B. Alonso. "Improved finger-knuckle-print authentication based on orientation enhancement." Electronics Letters 47, no. 6 (2011): 380. http://dx.doi.org/10.1049/el.2011.0156.

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34

Zhu, Le-qing, and San-yuan Zhang. "Multimodal biometric identification system based on finger geometry, knuckle print and palm print." Pattern Recognition Letters 31, no. 12 (2010): 1641–49. http://dx.doi.org/10.1016/j.patrec.2010.05.010.

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35

Anitha, M. L., and K. A. Radhakrishna. "Extraction of Region of Interest (ROI) for Palm Print and Inner Knuckle Print." International Journal of Computer Applications 124, no. 14 (2015): 21–26. http://dx.doi.org/10.5120/ijca2015905784.

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36

A., Muthukumar, and Kannan S. "FINGER KNUCKLE PRINT RECOGNITION WITH SIFT AND K-MEANS ALGORITHM." ICTACT Journal on Image and Video Processing 03, no. 03 (2013): 583–88. http://dx.doi.org/10.21917/ijivp.2013.0083.

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37

LIN, Sen, and Yuan WANG. "Finger-knuckle-print recognition based on fused pixels Gabor-Tetrolet." Chinese Journal of Liquid Crystals and Displays 36, no. 9 (2021): 1314–22. http://dx.doi.org/10.37188/cjlcd.2021-0015.

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38

Bahmed, Farah, and Madani Ould Mammar. "Basic finger inner‐knuckle print: A new hand biometric modality." IET Biometrics 10, no. 1 (2020): 65–73. http://dx.doi.org/10.1049/bme2.12000.

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39

Tao, Zhigang, Yingtian Hu, and Xiaoping Wang. "A new biometric system based on inner-knuckle-print recognition." IOP Conference Series: Earth and Environmental Science 252 (July 9, 2019): 032210. http://dx.doi.org/10.1088/1755-1315/252/3/032210.

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40

Zhang, Lin, Lei Zhang, David Zhang, and Zhenhua Guo. "Phase congruency induced local features for finger-knuckle-print recognition." Pattern Recognition 45, no. 7 (2012): 2522–31. http://dx.doi.org/10.1016/j.patcog.2012.01.017.

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41

Nigam, Aditya, Kamlesh Tiwari, and Phalguni Gupta. "Multiple texture information fusion for finger-knuckle-print authentication system." Neurocomputing 188 (May 2016): 190–205. http://dx.doi.org/10.1016/j.neucom.2015.04.126.

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42

Kim, Jooyoung, Kangrok Oh, Beom-Seok Oh, Zhiping Lin, and Kar-Ann Toh. "A Line Feature Extraction Method for Finger-Knuckle-Print Verification." Cognitive Computation 11, no. 1 (2018): 50–70. http://dx.doi.org/10.1007/s12559-018-9593-6.

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43

Jaswal, Gaurav, Amit Kaul, and Ravinder Nath. "Knuckle Print Biometrics and Fusion Schemes -- Overview, Challenges, and Solutions." ACM Computing Surveys 49, no. 2 (2016): 1–46. http://dx.doi.org/10.1145/2938727.

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44

Kim, Min-Ki. "Finger-Knuckle-Print Verification Using Vector Similarity Matching of Keypoints." Journal of Korea Multimedia Society 16, no. 9 (2013): 1057–66. http://dx.doi.org/10.9717/kmms.2013.16.9.1057.

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45

Altaher, Ali Salem, and Saleem Mohammed Ridha Taha. "Personal authentication based on finger knuckle print using quantum computing." International Journal of Biometrics 9, no. 2 (2017): 129. http://dx.doi.org/10.1504/ijbm.2017.085676.

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46

Taha, Saleem Mohammed Ridha, and Ali Salem Altaher. "Personal authentication based on finger knuckle print using quantum computing." International Journal of Biometrics 9, no. 2 (2017): 129. http://dx.doi.org/10.1504/ijbm.2017.10006482.

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47

Hanmandlu, Madasu, and Jyotsana Grover. "Feature Selection for Finger Knuckle Print-based Multimodal Biometric System." International Journal of Computer Applications 38, no. 10 (2012): 27–33. http://dx.doi.org/10.5120/4645-6905.

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48

Rituraj, Vishalakshi, and Dr Shyam Krishna Singh. "An Efficient Approach of Finger Knuckle Print Based Recognition Using Googlenet Model." International Journal for Research in Applied Science and Engineering Technology 10, no. 6 (2022): 817–20. http://dx.doi.org/10.22214/ijraset.2022.43639.

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Abstract: The need of personal recognition and identification became necessary in order to keep intruders away and to allow only the legitimate persons in order to protect privacy and to secure data from being altered. But, with the technological evolution and increasing risk of data theft, it has been a mandatory demand. A persons’s identity is not limited to his/her name, password or PIN but today, their biometrics are being used as an integral part of their identification. The emergence of Deep learning, a subset of Artificial Intelligence, brought a revolution in the field of computer visi

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49

Manjunath, S., D. S. Guru, K. B. Nagasundara, and M. G. Suraj. "2D2LPI." International Journal of Computer Vision and Image Processing 3, no. 2 (2013): 17–31. http://dx.doi.org/10.4018/ijcvip.2013040102.

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In this paper, a new method of representing images called two directional two dimensional locality preserving indexing called 2D2LPI is presented. It is an extension of the two dimensional locality preserving indexing (2DLPI) method. The authors argue that the recently proposed 2DLPI reduces the dimensions of images in row direction and we propose an alternate way of reducing the dimension in column direction. Later the authors propose a method to reduce the size of an image both in row and column directions. To corroborate the efficacy of the proposed two directional two dimensional approach

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50

Yu, Peng Fei, Hao Zhou, and Hai Yan Li. "Personal Identification Using Finger-Knuckle-Print Based on Local Binary Pattern." Applied Mechanics and Materials 441 (December 2013): 703–6. http://dx.doi.org/10.4028/www.scientific.net/amm.441.703.

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Over the last ten years, considerable progress has been made on the new hand-based biometric recognition, such as palmprint and hand vein. During this period, it has been proved that Finger-Knuckle-Print (FKP) can be used as a biometric identifier. In this paper, we present an effective FKP identification method based on Local Binary Pattern (LBP), whose idea is to divide the region of interest (ROI) of FKP into a set of sub-image blocks, which can be applied to extract the local features of the FKP. After that, LBP histograms of image blocks in a FKP ROI image are connected together to build

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