UPSI Digital Repository (UDRep)
|
|
|
Abstract : Universiti Pendidikan Sultan Idris |
This research aims to develop a new method to secure finger-vein (FV) biometric information during user authentication process against different types of network security attacks. Hence, a novel method was proposed to solve the existing problem in biometric authentication systems, which is the leakage of biometric information. Our proposed method meets information security definition standard requirements (CIA). The research design was carried out in two stages. In the first stage, the researcher developed a new merge algorithm in order to produce a new hybrid biometric pattern by merging Radio Frequency Identification (RFID) features with FV biometric features to increase the randomization, and enhance the security and structure of the new pattern. While, in the second stage, a new secure user verification method is developed based on blockchain technique, AES algorithm and a new steganography method that is based on Particle Swarm Optimization (PSO) algorithm. A dataset was used comprising of 6000 samples of FV images. The experimental results showed the effectiveness of the proposed authentication method. Where, this method achieved a high level of performance accuracy of 97.9% during the implementation of this method using FV biometric for 106 users. Furthermore, the proposed method has an advantage 55.56% higher than the benchmark method as a result of the comparison between the proposed method and benchmark method, depending on some security issues where, the proposed method covered 100% of these issues. Whereas, the benchmark method covered only 44.44% as indicated in Chapter 5. Moreover, the results showed that the structure of hybrid pattern is robust and immune towards detection by the attacker, and is flexible to being cancelable and reconstructed again in case of loss of this pattern. More so, the proposed method showed high resistance against spoofing and brute-force attacks. Clearly, such empirical results suggest that the FV information are confidential, integrated and available only for specific authorized persons only in the entire steps of the authentication process.The implication of this study is that, the proposed method can be applied in decentralised network architectures by eliminating the central point, and addressing the network failure problem and security at the same time. |
References |
Bhattacharya, T., Bhowmik, S., & Chaudhuri, S. R. B. (2008). A steganographic approach by using Session based Stego-Key, genetic algorithm and variable bit replacement technique. Proceedings of the 2008 International Conference on Computer and Electrical Engineering, ICCEE 2008, 51–55. https://doi.org/10.1109/ICCEE.2008.108
Chavez-Galaviz, J., Ruiz-Rojas, J., & Garcia-Gonzalez, A. (2015). Embedded biometric cryptosystem based on finger vein patterns. 2015 12th International Conference on Electrical Engineering, Computing Science and Automatic Control, CCE 2015, 1–6. https://doi.org/10.1109/ICEEE.2015.7357994
Cheng, Y., Chen, H., & Cheng, B. (2016). Special point representations for reducing data space requirements of finger-vein recognition applications. Multimedia Tools and Applications, 76(278). https://doi.org/10.1007/s11042-016-3300-y
Cresitello-Dittmar, B. (2016). Application of the Blockchain For Authentication and Verification of Identity. Retrieved from http://www.cs.tufts.edu/comp/116/archive/fall2016/bcresitellodittmar.pdf
Damavandinejadmonfared, S., Mobarakeh, A. K., Suandi, S. A., & Rosdi, B. A. (2012). Evaluate and determine the most appropriate method to identify finger vein. Procedia Engineering, 41(Iris), 516–521. https://doi.org/10.1016/j.proeng.2012.07.206
Dong, L., Yang, G., Yin, Y., Liu, F., & Xi, X. (2012). Finger Vein Verification Based on a Personalized Best Patches Map.
Dong, L., Yang, G., Yin, Y., Xi, X., Yang, L., & Liu, F. (2015). Finger Vein Verification with Vein Textons. International Journal of Pattern Recognition and Artificial Intelligence, 29(04), 1556003. https://doi.org/10.1142/S0218001415560030
Eberhart, R. C., & Yuhui Shi. (2001). Particle swarm optimization: developments, applications and resources. Proceedings of the 2001 Congress on Evolutionary Computation (IEEE Cat. No.01TH8546), 1, 81–86. https://doi.org/10.1109/CEC.2001.934374
Elshoura, S. M., & Megherbi, D. B. (2013). A secure high capacity full-gray-scale-level multi-image information hiding and secret image authentication scheme via Tchebichef moments. Signal Processing: Image Communication, 28(5), 531–552. https://doi.org/10.1016/j.image.2012.12.005
Eui Chul Lee, 1 Hyeon Chang Lee, 2 Kang Ryoung Park2. (2009). Finger Vein Recognition Using Minutia-Based Alignment and Local Binary Pattern-Based Feature Extraction. International Journal of Imaging Systems and Technology, 19(3), 179–186. https://doi.org/10.1002/ima.20193
Fard, a. M., & Varasteh-A., F. (2006). A New Genetic Algorithm Approach for Secure JPEG Steganography. 2006 IEEE International Conference on Engineering of Intelligent Systems, 1–6. https://doi.org/10.1109/ICEIS.2006.1703168
Fariba. (2013). Analysis of Methods for Finger Vein Recognition. (November), 128. Retrieved from http://etd.lib.metu.edu.tr/upload/12616645/index.pdf
Fateme Saadat, M. N. (2015). A Multibiometric Finger Vein Verification System Based On Score Level Fusion Strategy. Second International Congress on Technology, Communication and Knowledge (ICTCK 2015) November, 11-12, 2015 - Mashhad Branch, Islamic Azad University, Mashhad, Iran, (Ictck), 11–12.
Fayyaz, M., Hajizadeh-Saffar, M., Sabokrou, M., Hoseini, M., & Fathy, M. (2016). A novel approach for Finger Vein verification based on self-taught learning. Iranian Conference on Machine Vision and Image Processing, MVIP, 2016-Febru, 88–91. https://doi.org/10.1109/IranianMVIP.2015.7397511
Fernández-caramés, T. M., & Member, S. (2018). A Review on the Use of Blockchain for the Internet of Things. 3536(c), 1–23. https://doi.org/10.1109/ACCESS.2018.2842685
Ghasemi, E., & Shanbehzadeh, J. (2010). An imperceptible steganographic method based on Genetic Algorithm. 2010 5th International Symposium on Telecommunications, 836–839. https://doi.org/10.1109/ISTEL.2010.5734138
González, J. A., & Pino, R. (1999). Random number generator based on unpredictable chaotic functions. Computer Physics Communications, 120(2), 109–114. https://doi.org/10.1016/S0010-4655(99)00233-7
Goudelis, G., Tefas, A., & Pitas, I. (2009). Emerging biometric modalities: A survey. Journal on Multimodal User Interfaces, 2(3), 217–235. https://doi.org/10.1007/s12193-009-0020-x
Gupta, P., & Gupta, P. (2015). An accurate finger vein based verification system. Digital Signal Processing: A Review Journal, 38, 43–52. https://doi.org/10.1016/j.dsp.2014.12.003
Hartung, D., Martin, S., & Busch, C. (2011). Quality estimation for vascular pattern recognition. 2011 International Conference on Hand-Based Biometrics, ICHB 2011 - Proceedings, 258–263. https://doi.org/10.1109/ICHB.2011.6094332
He, M., Horng, S.-J., Fan, P., Run, R.-S., Chen, R.-J., Lai, J.-L., … Sentosa, K. O. (2010). Performance evaluation of score level fusion in multimodal biometric systems.
Pattern Recognition, 43(5), 1789–1800. https://doi.org/10.1016/j.patcog.2009.11.018
Horng, S. J., Chen, Y. H., Run, R. S., Chen, R. J., Lai, J. L., & Sentosal, K. O. (2009). An improved score level fusion in multimodal biometric systems. Parallel and Distributed Computing, Applications and Technologies, PDCAT Proceedings, (c), 239–246. https://doi.org/10.1109/PDCAT.2009.82
Huang, H., Liu, S., Zheng, H., Ni, L., Zhang, Y., & Li, W. (2017). DeepVein: Novel finger vein verification methods based on Deep Convolutional Neural Networks. 2017 IEEE International Conference on Identity, Security and Behavior Analysis, ISBA 2017, (5). https://doi.org/10.1109/ISBA.2017.7947683
Hussain, M., Abdul Wahab, A. W., Batool, I., & Arif, M. (2015). Secure password transmission for web applications over internet using cryptography and image steganography. International Journal of Security and Its Applications, 9(2), 179–188. https://doi.org/10.14257/ijsia.2015.9.2.17
Ibrahim, M. M. S., Al-namiy, F. S., Beno, M., & Rajaji, L. (2011). Biometric Authentication for secured Transaction using Finger Vein Technology. (Seiscon), 760–763.
Jadhav, M., & Nerkar, P. M. (2016). Implementation of an embedded hardware of FVRS on FPGA. Proceedings - IEEE International Conference on Information Processing, ICIP 2015, 48–53. https://doi.org/10.1109/INFOP.2015.7489349
Jagadiswary, D., & Saraswady, D. (2016). Biometric Authentication using Fused Multimodal Biometric. Procedia - Procedia Computer Science, 85(Cms), 109–116. https://doi.org/10.1016/j.procs.2016.05.187
Jain, M., & Kumar, A. (2017). RGB channel based decision tree grey-alpha medical image steganography with RSA cryptosystem. International Journal of Machine Learning and Cybernetics, 8(5), 1695–1705. https://doi.org/10.1007/s13042-016-0542-y
Jaiswal, S., Bhadauria, D. S. S., & Jadon, D. R. S. (2011). Biometric: Case Study. Journal of Global Research in Computer Science, 2(10), 19–48.
Jialiang Peng, Qiong Li, Ahmed A. Abd El-Latif, X. N. (2014). Finger multibiometric cryptosystems: fusion strategy and template security. Journal of Biomedical Optics, 19(2), 020901. https://doi.org/10.1117/1
Kanan, H. R., & Nazeri, B. (2014). A novel image steganography scheme with high embedding capacity and tunable visual image quality based on a genetic algorithm. Expert Systems with Applications, 41(14), 6123–6130. https://doi.org/10.1016/j.eswa.2014.04.022
Karimov, A. I., Butusov, D. N., Rybin, V. G., & Karimov, T. I. (2017). The study of the modified Chirikov map. Proceedings of 2017 20th IEEE International Conference on Soft Computing and Measurements, SCM 2017, (May), 341–344. https://doi.org/10.1109/SCM.2017.7970579
Khalil-Hani, M., & Eng, P. C. (2011). Personal verification using finger vein biometrics in FPGA-based System-on-Chip. 2011 7th International Conference on Electrical and Electronics Engineering (ELECO), II-171-II–176.
Khalil-Hani, M., & Lee, Y. H. (2013). FPGA embedded hardware system for finger vein biometric recognition. IECON Proceedings (Industrial Electronics Conference), 2273–2278. https://doi.org/10.1109/IECON.2013.6699485
Kshetri, N. (2017). Blockchain’s roles in strengthening cybersecurity and protecting privacy. Telecommunications Policy, (June), 1–12. https://doi.org/10.1016/j.telpol.2017.09.003
Kundi, D. S., Aziz, A., & Ikram, N. (2016). A high performance ST-Box based unified AES encryption/decryption architecture on FPGA. Microprocessors and Microsystems, 41, 37–46. https://doi.org/10.1016/j.micpro.2015.11.015
Li, X., Jiang, P., Chen, T., Luo, X., & Wen, Q. (2017). A survey on the security of blockchain systems. Future Generation Computer Systems. https://doi.org/10.1016/j.future.2017.08.020
Li, Z., Sun, D., Di, L., & Hao, L. (2010). Two modality-based bi-finger vein verification system. International Conference on Signal Processing Proceedings, ICSP, 1690– 1693. https://doi.org/10.1109/ICOSP.2010.5656847
Liu, F., Yang, G., Yin, Y., & Wang, S. (2014). Singular value decomposition based minutiae matching method for finger vein recognition. Neurocomputing, 145, 75–89. https://doi.org/10.1016/j.neucom.2014.05.069
Liu, T., Xie, J., Yan, W., Li, P., & Lu, H. (2015). Finger-vein pattern restoration with Direction-Variance-Boundary Constraint Search. Engineering Applications of Artificial Intelligence, 46, 131–139. https://doi.org/10.1016/j.engappai.2015.09.004
Liu, Z., Yin, Y., Wang, H., Song, S., & Li, Q. (2010). Finger vein recognition with manifold learning. Journal of Network and Computer Applications, 33(3), 275–282. https://doi.org/10.1016/j.jnca.2009.12.006
Lu, Y., Wu, S., Fang, Z., Xiong, N., Yoon, S., & Park, D. S. (2017). Exploring finger vein based personal authentication for secure IoT. Future Generation Computer Systems, 77, 149–160. https://doi.org/10.1016/j.future.2017.07.013
Lu, Y., Xie, S. J., Yoon, S., Wang, Z., & Park, D. S. (2013). An available database for the research of finger vein recognition. Proceedings of the 2013 6th International Congress on Image and Signal Processing, CISP 2013, 1(February 2015), 410–415. https://doi.org/10.1109/CISP.2013.6744030
Lu, Y., Xie, S. J., Yoon, S., Yang, J., & Park, D. S. (2013). Robust finger vein ROI localization based on flexible segmentation. Sensors (Switzerland), 13(11), 14339– 14366. https://doi.org/10.3390/s131114339
M. Khalil-Hani, Eng, P. C. (2010). FPGA-Based Embedded System Implementation of Finger Vein Biometrics. 2010 IEEE Symposium on Industrial Electronics and Applications (ISIEA 2010), October 3-5, 2010, Penang, Malaysia FPGA-Based, (Isiea), 700–705.
Marius Iulian Mihailescu. (2011). Vulnerabilities in Biometric Systems. 32. Retrieved from https://www.box.com/s/hesc4krm15yg36wvq375
Mathur, N., & Bansode, R. (2016). AES Based Text Encryption Using 12 Rounds with Dynamic Key Selection. Procedia Computer Science, 79, 1036–1043. https://doi.org/10.1016/j.procs.2016.03.131
Miura, N., Nagasaka, A., & Miyatake, T. (2004). Feature extraction of finger-vein patterns based on repeated line tracking and its application to personal identification. Machine Vision and Applications, 15(4), 194–203.
Mohd Asaari, M. S., Suandi, S. A., & Rosdi, B. A. (2014). Fusion of Band Limited Phase only Correlation and Width Centroid Contour Distance for finger based biometrics. Expert Systems with Applications, 41(7), 3367–3382. https://doi.org/10.1016/j.eswa.2013.11.033
Murakami, T., Ohki, T., & Takahashi, K. (2016). Optimal sequential fusion for multibiometric cryptosystems. Information Fusion, 32, 93–108. https://doi.org/10.1016/j.inffus.2016.02.002
N.Sugandhi, M.Mathankumar, V. P. (2014). Real Time Authentication System using Advanced Finger Vein Recognition Technique. International Conference on Communication and Signal Processing, April 3-5, 2014, India, 1183–1187.
Nandhinipreetha, A., & Radha, N. (2016). Multimodal biometric template authentication of finger vein and signature using visual cryptography. 2016 International Conference on Computer Communication and Informatics, ICCCI 2016, 7–10. https://doi.org/10.1109/ICCCI.2016.7479963
Naoto Miura, Nagasaka, A., & Miyatake, T. (2005). Extraction of Finger-Vein Patterns Using Maximum Curvature Points in Image Profile. Conference on Machine VIsion Applications, May 16-18, 2005 Tsukuba Science City, Japan, 34(4), 444–448.
Nassar, S. S., Ayad, N. M., Kelash, H. M., El-sayed, H. S., El-Bendary, M. A. M., Abd El- Samie, F. E., & Faragallah, O. S. (2016). Secure Wireless Image Communication Using LSB Steganography and Chaotic Baker Ciphering. Wireless Personal Communications, 91(3), 1023–1049. https://doi.org/10.1007/s11277-016-3387-5
Noori Hoshyar, A., & Sulaiman, R. (2011). Vein matching using artificial neural network in vein authentication systems. Conference on Graphic and Image Processing (ICGIP 2011), 8285(Icgip), 82850Z. https://doi.org/10.1117/12.913380
Ong, T. S., Teng, J. H., Muthu, K. S., & Teoh, A. B. J. (2013). Multi-instance finger vein recognition using minutiae matching. Proceedings of the 2013 6th International Congress on Image and Signal Processing, CISP 2013, 3(Cisp), 1730–1735. https://doi.org/10.1109/CISP.2013.6743955
Parthiban, K., Wahi, A., Sundaramurthy, S., & Palanisamy, C. (2014). Finger vein extraction and authentication based on gradient feature selection algorithm. 5th International Conference on the Applications of Digital Information and Web Technologies, ICADIWT 2014, 143–147. https://doi.org/10.1109/ICADIWT.2014.6814681
Patil, P., Narayankar, P., Narayan, D. G., & Meena, S. M. (2016). A Comprehensive Evaluation of Cryptographic Algorithms: DES, 3DES, AES, RSA and Blowfish. Procedia Computer Science, 78(December 2015), 617–624. https://doi.org/10.1016/j.procs.2016.02.108
Peng, J., El-Latif, A. A. A., Li, Q., & Niu, X. (2014). Multimodal biometric authentication based on score level fusion of finger biometrics. Optik, 125(23), 6891–6897. https://doi.org/10.1016/j.ijleo.2014.07.027
Peng, J., Wang, N., El-Latif, A. a. A., Li, Q., & Niu, X. (2012). Finger-vein Verification Using Gabor Filter and SIFT Feature Matching. 2012 Eighth International Conference on Intelligent Information Hiding and Multimedia Signal Processing, 45–48. https://doi.org/10.1109/IIH-MSP.2012.17
Pflug, A., Hartung, D., & Busch, C. (2012). Feature extraction from vein images using spatial information and chain codes. Information Security Technical Report, 17(1–2), 26–35. https://doi.org/10.1016/j.istr.2012.02.003
Pujari, M. A. A., & Shinde, M. S. S. (2016). Data Security using Cryptography and Steganography. IOSR Journal of Computer Engineering, 18(04), 130–139. https://doi.org/10.9790/0661-180405130139
Qin, H., & A. El Yacoubi, M. (2017). Deep Representation for Finger-vein Image Quality Assessment. IEEE Transactions on Circuits and Systems for Video Technology, 8215(c), 1. https://doi.org/10.1109/TCSVT.2017.2684826
Qin, H., & El-Yacoubi, M. A. (2015). Finger-Vein Quality Assessment by Representation Learning from Binary Images. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 9489, pp. 421–431). https://doi.org/10.1007/978-3-319-26532-2_46
Qin, H., & El-Yacoubi, M. A. (2017). Deep Representation-Based Feature Extraction and Recovering for Finger-Vein Verification. IEEE Transactions on Information Forensics and Security, 12(8), 1816–1829. https://doi.org/10.1109/TIFS.2017.2689724
Qin, H., He, X., Yao, X., & Li, H. (2017). Finger-vein verification based on the curvature in Radon space. Expert Systems with Applications, 82, 151–161. https://doi.org/10.1016/j.eswa.2017.03.068
Qiu, X., Kang, W., Tian, S., Jia, W., & Huang, Z. (2018). Finger Vein Presentation Attack Detection Using Total Variation Decomposition. IEEE Transactions on Information Forensics and Security, 13(2), 465–477. https://doi.org/10.1109/TIFS.2017.2756598
Raghavendra, R., Raja, K. B., Surbiryala, J., & Busch, C. (2014a). A low-cost multimodal biometric sensor to capture finger vein and fingerprint. IJCB 2014 - 2014 IEEE/IAPR International Joint Conference on Biometrics. https://doi.org/10.1109/BTAS.2014.6996225
Raghavendra, R., Raja, K. B., Surbiryala, J., & Busch, C. (2014b). Finger vascular pattern imaging - A comprehensive evaluation. 2014 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA 2014. https://doi.org/10.1109/APSIPA.2014.7041520
Raghavendra, R., Surbiryala, J., Raja, K. B., & Busch, C. (2014). Novel finger vascular pattern imaging device for robust biometric verification. IST 2014 - 2014 IEEE International Conference on Imaging Systems and Techniques, Proceedings, 148– 152. https://doi.org/10.1109/IST.2014.6958463
Razzak, M. I., & Yusof, R. (2010). Multimodal face and finger veins biometric authentication. Scientific Research and Essays, 5(17), 2529–2534. Retrieved from http://www.academicjournals.org/SRE/PDF/pdf2010/4Sep/Razzak%5Cnet%5Cnal.p df
Rosdi, B. A., Shing, C. W., & Suandi, S. A. (2011). Finger Vein Recognition Using Local Line Binary Pattern. Sensors, 11(12), 11357–11371. https://doi.org/10.3390/s111211357
Shen, H., Shen, J., Khan, M. K., & Lee, J.-H. (2017). Efficient RFID Authentication Using Elliptic Curve Cryptography for the Internet of Things. Wireless Personal Communications, 96(4), 5253–5266. https://doi.org/10.1007/s11277-016-3739-1
Singh, G. (2013). A Study of Encryption Algorithms (RSA, DES, 3DES and AES) for Information Security. International Journal of Computer Applications, 67(19), 975– 8887. https://doi.org/10.5120/11507-7224
Skillen, A., & Mannan, M. (2014). Mobiflage: Deniable storage encryption for mobile devices. IEEE Transactions on Dependable and Secure Computing, 11(3), 224–237. https://doi.org/10.1109/TDSC.2013.56
Song, W., Kim, T., Kim, H. C., Choi, J. H., Kong, H. J., & Lee, S. R. (2011). A finger-vein verification system using mean curvature. Pattern Recognition Letters, 32(11), 1541– 1547. https://doi.org/10.1016/j.patrec.2011.04.021
Suzuki, H., Suzuki, M., Urabe, T., & Obi, T. (2013). Secure biometric image sensor and authentication scheme based on compressed sensing. Applied Optics, 52(33), 8161– 8168. https://doi.org/Doi 10.1364/Ao.52.008161
Tang, D., Huang, B., Li, R., & Li, W. (2010). A person retrieval solution using finger vein patterns. Proceedings - International Conference on Pattern Recognition, 1306–1309. https://doi.org/10.1109/ICPR.2010.325
Tang, D., Huang, B., Li, R., Li, W., & Li, X. (2012). Finger vein verification using Occurrence Probability Matrix (OPM). Proceedings of the International Joint Conference on Neural Networks, 21–26. https://doi.org/10.1109/IJCNN.2012.6252518
Tang, D., Huang, B., Li, W., & Li, X. (2012). A method of evolving finger vein template. Proceedings - 2012 International Symposium on Biometrics and Security Technologies, ISBAST 2012, 96–101. https://doi.org/10.1109/ISBAST.2012.21
Tian, F. (2016). An Agri-food Supply Chain Traceability System for China Based on RFID & Blockchain Technology. 2016 13th International Conference on Service Systems and Service Management (ICSSSM), 1–6. https://doi.org/10.1109/ICSSSM.2016.7538424
Tseng, L. Y., Chan, Y. K., Ho, Y. A., & Chu, Y. P. (2008). Image hiding with an improved genetic algorithm and an optimal pixel adjustment process. Proceedings - 8th International Conference on Intelligent Systems Design and Applications, ISDA 2008, 3, 320–325. https://doi.org/10.1109/ISDA.2008.235
Vard, A. R., Moallem, P., & Nilchi, A. R. N. (2009). Texture-based parametric active contour for target detection and tracking. International Journal of Imaging Systems and Technology, 19(3), 179–186. https://doi.org/10.1002/ima.20193
Von Solms, R., & Van Niekerk, J. (2013). From information security to cyber security. Computers and Security, 38, 97–102. https://doi.org/10.1016/j.cose.2013.04.004
Walu?, M., Bernacki, K., & Konopacki, J. (2017). Impact of NIR wavelength lighting in image acquisition on finger vein biometric system effectiveness. Opto-Electronics Review, 25(4), 263–268. https://doi.org/10.1016/j.opelre.2017.07.003
Wang, J., Xiao, J., Lin, W., & Luo, C. (2015). Discriminative and generative vocabulary tree: With application to vein image authentication and recognition. Image and Vision Computing, 34, 51–62. https://doi.org/10.1016/j.imavis.2014.10.014
Wang, S., Yang, B., & Niu, X. (2010). A Secure Steganography Method based on Genetic Algorithm. 1(1), 28–35.
Wang, Y., Wang, L., & Xue, C. ao. (2018). Medical information security in the era of artificial intelligence. Medical Hypotheses, 115(February), 58–60. https://doi.org/10.1016/j.mehy.2018.03.023
William, A., Ong, T. S., Lau, S. H., & Goh, M. K. O. (2016). Finger Vein verification using local histogram of hybrid texture descriptors. IEEE 2015 International Conference on Signal and Image Processing Applications, ICSIPA 2015 - Proceedings, 304–308. https://doi.org/10.1109/ICSIPA.2015.7412209
Williamson, “Avery, Tsay, L.-S., Kateeb, I. A., & Burton”, L. (2013). “Solutions for RFID Smart Tagged Card Security Vulnerabilities.” AASRI Procedia, 4, 282–287. https://doi.org/10.1016/j.aasri.2013.10.042
Wu, J. Da, & Liu, C. T. (2011a). Finger-vein pattern identification using principal component analysis and the neural network technique. Expert Systems with Applications, 38(5), 5423–5427. https://doi.org/10.1016/j.eswa.2010.10.013
Wu, J. Da, & Liu, C. T. (2011b). Finger-vein pattern identification using SVM and neural network technique. Expert Systems with Applications, 38(5), 5423–5427. https://doi.org/10.1016/j.eswa.2010.10.013
Wu, J. Da, & Ye, S. H. (2009). Driver identification using finger-vein patterns with Radon transform and neural network. Expert Systems with Applications, 36(3 PART 2), 5793–5799. https://doi.org/10.1016/j.eswa.2008.07.042
Wu, J.-D., Liu, C.-T., Tsai, Y.-J., Liu, J.-C., & Chang, Y.-W. (2010). Development of neural network techniques for finger-vein pattern classification. SECOND INTERNATIONAL CONFERENCE ON DIGITAL IMAGE PROCESSING 26-28 February 2010 Singapore, Singapore, 7546, 75460F. https://doi.org/10.1117/12.852799
Wu, Z., Tian, L., Li, P., Wu, T., Jiang, M., & Wu, C. (2016). Generating stable biometric keys for flexible cloud computing authentication using finger vein. Information Sciences, 0, 1–17. https://doi.org/10.1016/j.ins.2016.12.048
Xi, X., Yang, L., & Yin, Y. (2017). Learning discriminative binary codes for finger vein recognition. Pattern Recognition, 66(July 2016), 26–33. https://doi.org/10.1016/j.patcog.2016.11.002
Xin, Y., Liu, Z., Zhang, H., & Zhang, H. (2012). Finger vein verification system based on sparse representation. Applied Optics, 51(25), 6252–6258. https://doi.org/10.1364/AO.51.006252
Yang, J., Shi, Y., & Yang, J. (2011). Personal identification based on finger-vein features. Computers in Human Behavior, 27(5), 1565–1570. https://doi.org/10.1016/j.chb.2010.10.029
Yang, L., Yang, G., Yin, Y., & Xi, X. (2014). Exploring soft biometric trait with finger vein recognition. Neurocomputing, 135, 218–228. https://doi.org/10.1016/j.neucom.2013.12.029
Yang, W., Huang, X., Zhou, F., & Liao, Q. (2014). Comparative competitive coding for personal identification by using finger vein and finger dorsal texture fusion q. Information Sciences, 268, 20–32. https://doi.org/10.1016/j.ins.2013.10.010
Yang, X., Li, Z., Wang, A., & Wen, S. (2011). Design research of the des against power analysis attacks based on FPGA. Microprocessors and Microsystems, 35(1), 18–22. https://doi.org/10.1016/j.micpro.2010.11.002
Ye, Y., Zheng, H., Ni, L., Liu, S., & Li, W. (2016). A study on the individuality of finger vein based on statistical analysis. 2016 International Conference on Biometrics, ICB 2016, 1–5. https://doi.org/10.1109/ICB.2016.7550089
Yun-peng, Z. (2009). Digital Image Encryption Algorithm Based on Chaos and Improved DES. (October), 480–485.
Zhang, F., Guo, S., & Qian, X. (2010). Segmentation for finger vein image based on PDEs denoising. Proceedings - 2010 3rd International Conference on Biomedical Engineering and Informatics, BMEI 2010, 2(Bmei), 531–535. https://doi.org/10.1109/BMEI.2010.5639983
Zheng, H., Ni, L., Xian, R., Liu, S., & Li, W. (2015). BMDT: An optimized method for Biometric Menagerie Detection. 2015 IEEE 7th International Conference on Biometrics Theory, Applications and Systems, BTAS 2015. https://doi.org/10.1109/BTAS.2015.7358751 |
This material may be protected under Copyright Act which governs the making of photocopies or reproductions of copyrighted materials. You may use the digitized material for private study, scholarship, or research. |