Optimization of Three-dimensional Face Recognition Algorithms in Financial Identity Authentication

  • Cong Luo Guizhou University of Finance and Economics, Guiyang, Guizhou, China
  • Xiangbo Fan Guizhou University of Finance and Economics, Guiyang, Guizhou, China
  • Ying Yan Guizhou University of Finance and Economics, Guiyang, Guizhou, China
  • Han Jin Guizhou University of Finance and Economics, Guiyang, Guizhou, China
  • Xuan Wang Guizhou University of Finance and Economics, Guiyang, Guizhou, China

Abstract

Identity authentication is one of the most basic components in the computer network world. It is the key technology of information security. It plays an important role in the protection of system and data security. Biometric recognition technology provides a reliable and convenient way for identity authentication. Compared with other biometric recognition technologies, face recognition has become a hot research topic because of its convenience, friendliness and easy acceptance. With the maturity and progress of face recognition technology, its commercial application has become more and more widespread. Internet finance, e-commerce and other asset-related areas have begun to try to use face recognition technology as a means of authentication, so people’s security needs for face recognition systems are also increasing. However, as a biometric recognition system, face recognition system still has inherent security vulnerabilities and faces security threats such as template attack and counterfeit attack. In view of this, this paper studies the application of threedimensional face recognition algorithm in the field of financial identity authentication. On the basis of feature extraction of face information using neural network algorithm, K-L transform is applied to image high-dimensional vector mapping to make face recognition clearer. Thus, the image loss can be reduced.

References

[1] Nair, K. K.; Helberg, A.; Johannes, Vdm (2016). An approach to Improve the Match-on-Card Fingerprint Authentication System security, Sixth International Conference on Digital Information & Communication Technology & Its Applications, 119-125, 2016.
https://doi.org/10.1109/DICTAP.2016.7544012

[2] Rila, L.; Mitchell, C. J. (2003). Security protocols for biometrics-based cardholder authentication in smartcards, International Conference on Applied Cryptography and Network Security,Springer, 254-264,2003.
https://doi.org/10.1007/978-3-540-45203-4_20

[3] Nair, S.K.; Dashti, M.T.; Crispo, B.; et al. (2007). A hybrid PKI-IBC based ephemerizer system, IFIP International Information Security Conference,Springer, 241-252,2007.
https://doi.org/10.1007/978-0-387-72367-9_21

[4] Shi, J.P. (2016). Research on fingerprint watermarking algorithm for copyright protection and identity authentication, Nanjing University of Aeronautics and Astronautics, 2016.

[5] Li, Y.; Wang, Y.H.; Liu, J. (2011). 3D Face Recognition Based on Ridge Valley Feature Extraction, Computer Engineering and Applications, 47 (12), 7-112, 2011.

[6] As'Ari, M.A.; Sheikh, U.U.; Supriyanto, E. (2014). 3D shape descriptor for object recognition based on Kinect-like depth image, Image & Vision Computing, 32(4), 260-269, 2014.
https://doi.org/10.1016/j.imavis.2014.02.002

[7] Zhan, S.; Zhang, Q.X.; Jiang, J.G. (2013). Three-dimensional face recognition based on Gabor feature kernels cooperative expression, Journal of Photonics, 42 (12), 1448-1453, 2013.
https://doi.org/10.3788/gzxb20134212.1448

[8] Daniyal, F.; Nair, P.; Cavallaro, A.(2009). Compact Signatures for 3D Face Recognition under Varying Expressions, Sixth IEEE International Conference on Advanced Video and Signal Based Surveillance, AVSS 2009, 2-4 September 2009, Genova, Italy, 2009.
https://doi.org/10.1109/AVSS.2009.71

[9] Lu,S.W.; Da F.P.; Deng, X. (2015). Three-dimensional face recognition based on improved LBP, Journal of Southeast University: Natural Science Edition, 45 (4), 678-682, 2015.

[10] Karhunen,J.; Joutsensalo,J. (1994). Representation and separation of signals using nonlinear PCA type learning, Neural Networks, 7(1), 113-127, 1994.
https://doi.org/10.1016/0893-6080(94)90060-4

[11] Moon,H.; Phillips,P.J.(2001). Computational and performance aspects of PCA-based facerecognition algorithms, Perception, 30(3), 303-21, 2001.
https://doi.org/10.1068/p2896

[12] Velkumar,K.; Bhavani,M.(2012). Face Recognition Using PCA and LDA Algorithm, Second International Conference on Advanced Computing & Communication Technologies, 2012. Le Thi, Hoai An and Nguyen, M. C.

[13] Le T.; Hoai, A.;Nguyen, M.C.(2017). DCA based algorithms for feature selection in multi-class support vector machine, Annals of Operations Research, 249(1-2), 1-28, 2017.
https://doi.org/10.1007/s10479-016-2333-y

[14] Sasagawa, Y.(2014). Neural network system, Annals of Operations Research, 2014.

[15] Bevilacqua, V.; Cariello, L.; Carro, G.; Daleno, D.; Mastronardi, G.(2008). A face recognition system based on Pseudo 2D HMM applied to neural network coefficients, Soft Computing, 12(7), 615-621, 2008.
https://doi.org/10.1007/s00500-007-0253-0

[16] Aitkenhead, M.J.; Mcdonald, A.(2003). A neural network face recognition system, Engineering Applications of Artificial Intelligence, 16(3), 167-176, 2003.
https://doi.org/10.1016/S0952-1976(03)00042-3

[17] Soltanali, S.; Halladj, R.; Tayyebi, S.; Rashidi, A.(2014). Neural network and genetic algorithm for modeling and optimization of effective parameters on synthesized ZSM-5 particle size, Materials Letters, 136(136), 138-140, 2014.
https://doi.org/10.1016/j.matlet.2014.08.039

[18] Wilkinson, R.; El, S.; Gieseking, C.(2010). Performance and Arousal as a Function of Incentive, Information Load, and Task Novelty, Psychophysiology, 9(6), 589-599, 2010.
https://doi.org/10.1111/j.1469-8986.1972.tb00768.x

[19] Reed, I.S.; Mallett, J.D.; Brennan, L.E.(2007). Rapid Convergence Rate in Adaptive Arrays, IEEE Transactions on Aerospace & Electronic Systems, AES-10(6), 853-863, 2007.
https://doi.org/10.1109/TAES.1974.307893

[20] Soudry, D.; Di Castro, D.; Gal, A.; Kolodny, A.; Kvatinsky, S.(2017). Memristor-Based Multilayer Neural Networks With Online Gradient Descent Training, IEEE Transactions on Neural Networks & Learning Systems, 26(10), 2408-2421, 2017.
https://doi.org/10.1109/TNNLS.2014.2383395
Published
2022-03-21
How to Cite
LUO, Cong et al. Optimization of Three-dimensional Face Recognition Algorithms in Financial Identity Authentication. INTERNATIONAL JOURNAL OF COMPUTERS COMMUNICATIONS & CONTROL, [S.l.], v. 17, n. 3, mar. 2022. ISSN 1841-9844. Available at: <http://univagora.ro/jour/index.php/ijccc/article/view/3744>. Date accessed: 22 may 2022. doi: https://doi.org/10.15837/ijccc.2022.3.3744.

Keywords

Face Recognition, Principal Component Analysis, BP Neural Network, Three-dimensional Face