Warianty tytułu
Skuteczna identyfikacja osób za pomocą dwu- i trójwymiarowych kostek palców
Języki publikacji
Abstrakty
Because of their high level of precision, biometric systems continue to attract the attention of several researchers. Different biometric traits have been investigated for use in security systems, such as fingerprints, faces, irises, palmprints, and knuckle prints. In most cases, bi-dimensional information is utilized. To achieve this aim, we have examined the performance of biometric identification systems based on a 3D-FKP database through five pre-trained networks such as AlexNet, VGG19, GoogleNet, ResNet50, and DenseNet201. The obtained experimental results illustrate the effectiveness of the suggested approach, with a high recognition rate and accuracy.
Ze względu na wysoki poziom precyzji systemy biometryczne nadal przyciągają uwagę wielu badaczy. Zbadano różne cechy biometryczne pod kątem wykorzystania w systemach bezpieczeństwa, takie jak odciski palców, twarze, tęczówki, odciski dłoni i odciski kostek. W większości przypadków wykorzystuje się informacje dwuwymiarowe. Aby osiągnąć ten cel, zbadaliśmy wydajność systemów identyfikacji biometrycznej opartych na bazie danych 3D-FKP za pośrednictwem pięciu wstępnie wyszkolonych sieci, takich jak AlexNet, VGG19, GoogleNet, ResNet50 i DenseNet201. Uzyskane wyniki eksperymentalne ilustrują skuteczność zaproponowanego podejścia, przy wysokim współczynniku rozpoznawania i dokładności.
Czasopismo
Rocznik
Tom
Strony
62--68
Opis fizyczny
Bibliogr. 40 poz., rys., tab.
Twórcy
autor
- Automatic Laboratory, Department of Electrical Engineering, University of 20 August 1955, Skikda Algeria, samai.djamel@univ-ouargla.dz
autor
- Univ Ouargla, Fac.des nouvelles technologies de l’information et de la communication. Lab. de G´enie Electrique (LAGE), 30000, Ouargla, Algeria, samai.djamel@univ-ouargla.dz
autor
- Univ Ouargla, Fac.des nouvelles technologies de l’information et de la communication. Lab. de G´enie Electrique (LAGE), 30000, Ouargla, Algeria, korichi.maarouf@univ-ouargla.dz
autor
- Laboratory of Mathematics, Informatics and Systems (LAMIS), Larbi Tebessi University, 12002 Tebessa, Algeria, ameraoumia@gmail.com
autor
- Automatic Laboratory, Department of Electrical Engineering, University of 20 August 1955, Skikda Algeria, alachouri@yahoo.fr
Bibliografia
- [1] Anil Jain, Arun Ross, and Salil Prabhakar. Fingerprint matching using minutiae and texture features. In Proceedings 2001 International Conference on Image Processing (Cat. No.01CH37205), vol 3, pages 282–285. IEEE, 2001.
- [2] Anil K Jain, Jianjiang Feng, and Karthik Nandakumar. Fingerprint matching. Computer, 43(2):36–44, 2010.
- [3] John Daugman. New methods in iris recognition. IEEE Trans on Systems, Man, and Cybernetics, Part B (Cybernetics), 37(5):1167–1175, 2007.
- [4] Kien Nguyen, Clinton Fookes, Raghavender Jillela, Sridha Sridharan, and Arun Ross. Long range iris recognition: A survey. Pattern Recognition, 72:123–143, 2017.
- [5] Halvor Borgen, Patrick Bours, and Stephen D Wolthusen. Visible-spectrum biometric retina-recognition. In 2008 International Conference on Intelligent Information Hiding and Multimedia Signal Processing, pages 1056–1062. IEEE, 2008.
- [6] Adams Kong, David Zhang, and Mohamed Kamel. A survey of palmprint recognition. patternrecognition, 42(7):1408–1418, 2009.
- [7] Dexing Zhong, Xuefeng Du, and Kuncai Zhong. Decade progress of palmprint recognition: A brief survey. Neurocomputing, 328:16–28, 2019.
- [8] Nidhi Saxena, Vipul Saxena, Neelesh Dubey, and Pragya Mishra. Hand geometry: A new method for biometric recognition. International Journal of Soft Computing and Engineering (IJSCE), 2(6):2231–2307, 2013.
- [9] Harry Wechsler, Jonathon P Phillips, Vicki Bruce, FrancoiseFogelman Soulie, and Thomas S Huang. Face recognition: From theory to applications, volume 163. Springer Science & Business Media, 2012.
- [10] Attila Andics, James M McQueen, Karl Magnus Petersson, Viktor G´al, G´abor Rudas, and Zolt´an Vidny´anszky. Neural mechanisms for voice recognition. Neuroimage, 52(4):1528–1540, 2010.
- [11] Ali Karouni, Bassam Daya, and Samia Bahlak. Ofine signature recognition using neural networks approach. Procedia Computer Science, 3:155–161, 2011.
- [12] Sushmita Mitra and Tinku Acharya. Gesture recognition: A survey. IEEE Transactions onSystems, Man, and Cybernetics, Part C (Applications and Reviews), 37(3):311–324, 2007.
- [13] Lin Zhang, Lei Zhang, David Zhang, and Hailong Zhu. Ensemble of local and global information for fnger–knuckle-print recognition. Pattern recognition, 44(9):1990–1998, 2011.
- [14] Yikui Zhai, He Cao, Lu Cao, Hui Ma, Junyin Gan, Junying Zeng, Vincenzo Piuri, Fabio Scotti, Wenbo Deng, Yihang Zhi, et al. A novel fnger-knuckle-print recognition based on batchnor-malized cnn. In Chinese conference on biometric recognition, pages 11–21. Springer, 2018.
- [15] A Zohrevand, Z Imani, and M Ezoji. Deep convolutional neural network for finger-knuckle-print recognition. International Journal of Engineering, 34(7):1684–1693, 2021.
- [16] Feng Liu, David Zhang, and Linlin Shen. Study on novel curvature features for 3d fngerprint recognition. Neurocomputing, 168:599–608, 2015.
- [17] Xuefei Yin, Yanming Zhu, and Jiankun Hu. 3d fngerprint recognition based on ridge-valley-guided 3d reconstruction and 3d topology polymer feature extraction. IEEE transactions on patternanalysis and machine intelligence, 43(3):1085–1091, 2019.
- [18] Wei Li, Lei Zhang, and David Zhang. Three dimensional palmprint recognition. In 2009 IEEEInternational Conference on Systems, Man and Cybernetics, pages 4847–4852. IEEE, 2009.
- [19] Lin Zhang, Ying Shen, Hongyu Li, and Jianwei Lu. 3d palmprint identifcation using block-wise features and collaborative representation. IEEE transactions on pattern analysis and machineintelligence, 37(8):1730–1736, 2014.
- [20] Djamel Samai, Khaled Bensid, Abdallah Meraoumia, Abdelmalik Taleb-Ahmed, and Mouldi Bedda. 2d and 3d palmprint recognition using deep learning method. In 2018 3rd InternationalConference on Pattern Analysis and Intelligent Systems (PAIS), pages 1–6. IEEE, 2018.
- [21] Mourad Chaa, Zahid Akhtar, and Abdelouahab Attia. 3d palmprint recognition using unsupervised convolutional deep learning network and svm classifer. IET Image Processing, 13(5):736–745, 2019.
- [22] Alize Scheenstra, Arnout Ruifrok, and Remco C Veltkamp. A survey of 3d facerecognition methods. In International Conference on Audio-and Video-based Biometric PersonAuthentication, pages 891–899. Springer, 2005.
- [23] Song Zhou and Sheng Xiao. 3d face recognition: a survey. Human-centric Computing andInformation Sciences, 8(1):1–27, 2018.
- [24] Hui Chen and Bir Bhanu. Human ear recognition in 3d. IEEE Transactions on Pattern Analysisand Machine Intelligence, 29(4):718–737, 2007.
- [25] Iyyakutti Iyappan Ganapathi, Syed Sadaf Ali, Ngoc-Son Vu, Surya Prakash, and Naoufel Werghi. A survey of 3d ear recognition techniques. ACM Computing Surveys (CSUR), 2022.
- [26] Kevin HM Cheng and Ajay Kumar. Contactless biometric identifcation using 3d fnger knuckle patterns. IEEE transactions on pattern analysis and machine intelligence, 42(8):1868–1883, 2019.
- [27] Kevin HM Cheng and Ajay Kumar. Efcient and accurate 3d fnger knuckle matching using surface key points. IEEE Transactions on Image Processing, 29:8903–8915, 2020.
- [28] Kevin HM Cheng and Ajay Kumar. Deep feature collaboration for challenging 3d fnger knuckle identifcation. IEEE Transactions on Information Forensics and Security, 16:1158–1173, 2020.
- [29] Kevin HM Cheng and Ajay Kumar. Accurate 3d fnger knuckle recognition using auto-generated similarity functions. IEEE Transactions on Biometrics, Behavior, and Identity Science, 3(2):203–213, 2021.
- [30] Mourad Chaa, Zahid Akhtar, and Abdehai Lati. Contactless person recognition using 2d and 3d fnger knuckle patterns. Multimedia Tools and Applications, 81(6):8671–8689, 2022.
- [31] Kumar. FKP 3D database. https://www4.-comp.polyuedu.hk/~csajayk/ 3DKnuckle.htm, 2019 (accessed September 30, 2022).
- [32] Robert T. Frankot and Rama Chellappa. A method for enforcing integrability in shape from shading algorithms. IEEE Transactions on pattern analysis and machine intelligence, 10(4):439– 451, 1988.
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- [34] Farhana Sultana, Abu Sufan, and Paramartha Dutta. Advancements in image classifcation using convolutional neural network. In 2018 Fourth International Conference on Research inComputational Intelligence and Communication Networks (ICRCICN), pages 122–129. IEEE, 2018.
- [38] Bhavesh Pandya, Georgina Cosma, Ali A Alani, Aboozar Taherkhani, Vinayak Bharadi, and TM McGinnity. Fingerprint classifcation using a deep convolutional neural network. In 20184th International Conference on Information Management (ICIM), pages 86–91. IEEE, 2018.
- [39] Maram G Alaslani. Convolutional neural network based feature extraction for iris recognition. International Journal of Computer Science & Information Technology (IJCSIT) Vol, 10, 2018.
- [40] Rondik J Hassan, Adnan Mohsin Abdulazeez, et al. Deep learning convolutional neural network for face recognition: A review. International Journal of Science and Business, 5(2):114–127, 2021.
- [41] Ajay Kumar and Ch Ravikanth. Personal authentication usingfnger knuckle surface. IEEETransactions on Information Forensics and Security, 4(1):98–110, 2009.
- [42] Waziha Kabir, M Omair Ahmad, and MNS Swamy. Normalization and weighting techniques based on genuine-impostor score fusion in multi-biometric systems. IEEE Trans on Information Forensics and Security, 13(8):1989–2000, 2018.
- [43] Ted Dunstone and Neil Yager. Biometric system and data analysis: Design, evaluation, and data mining. Springer, 2009.
Typ dokumentu
Bibliografia
Identyfikatory
Identyfikator YADDA
bwmeta1.element.baztech-a7785f99-99b9-41b9-9c49-c4835d2755b7