POTENTIALS OF ONE STEP SECANT AND FLETCHER-POWELL CONJUGATE GRADIENT BACK PROPAGATION ALGORITHMS FOR MULTIMODAL BIOMETRIC RECOGNITION APPLICATIONS

Authors
  • Esan, O. A

    The Federal University of Technology, Akure, Ondo State, Nigeria

  • Adedeji, K. B

    The Federal University of Technology, Akure, Ondo State, Nigeria

  • Itodo, E. S.

    The Federal University of Technology, Akure, Ondo State, Nigeria

Keywords:
Array, Array, Array, Array, Array, Array
Abstract

There has been an increased interest in reliable methods of biometric identification due to the growing emphasis on security and the increasing prevalence of identity theft. A multimodal system which combines several physiological traits of an individual could improve the security level. Most commercial biometric systems use artificial neural network (ANN) techniques due to their robustness to parameter changes. The performance of such a system depends on the learning algorithm. In this paper, the potential of Fletcher Powell Conjugate Gradient (FPCG) and One Step Secant (OSS) backpropagation learning algorithms for a multimodal hand geometry recognition application is considered and analysed. The effectiveness of these algorithms is assessed using the mean square error, image gradient, error histogram, and regression. Simulation results show that the FPCG algorithm has better performance than the OSS algorithm in terms of the mean square error, regression, and image gradient. The mean square error due to the FPCG is lower than that of the OSS algorithm. This indicates that there is about 76% improvement in the mean square error if the FPCG algorithm is selected over the OSS. Also, the regression analysis shows that the FPCG algorithm is more accurate than the OSS algorithm. However, in terms of speed of convergence, the OSS algorithm has the lowest number of epochs to attain best performance. Among these two backpropagation algorithms, the FPCG is considered more suitable for multimodal hand geometry identification.

 

 

Author Biographies
  1. Esan, O. A, The Federal University of Technology, Akure, Ondo State, Nigeria

    Department of Computer Engineering

  2. Adedeji, K. B, The Federal University of Technology, Akure, Ondo State, Nigeria

    Department of Electrical and Electronics Engineering

  3. Itodo, E. S., The Federal University of Technology, Akure, Ondo State, Nigeria

    Department of Electrical and Electronics Engineering

References

Abed, M.H. (2017). Wrist and Palm Vein Pattern Recognition using Gabor Filter. Journal of AL-Qadisiyah for Computer Science and Mathematics, 9(1), 49-60.

Abid, S., Mouelhi, A. and Fnaiech, F. (2006). Accelerating the Multilayer Perceptron Learning with the Davidon Fletcher Powell Algorithm. In: Proceedings of the IEEE International Joint Conference on Neural Network, July 16-21, 2006, Vancouver, Canada, pp. 3389-3394.

Abiodun, O.I., Jantan, A., Omolara, A.E., Dada, K.V., Mohamed, N. and Arshad, H. (2018). State-of-the-Art in Artificial Neural Network Applications: A Survey. Heliyon, 4(11), 1-41.

Abozaid, A., Haggag, A., Kasban, H. and Eltokhy, M. (2019). Multimodal Biometric Scheme for Human Authentication Technique based on Voice and Face Recognition Fusion, Multimedia Tools and Applications, 78(12), 16345-16361.

Afifi, M. (2019). IIK Hands: Gender Recognition and Biometric Identification using a Large Dataset of Hand Images. Multimedia Tools and Applications, 78(15), 20835-20854.

COEP Palm Print Database. College of Engineering, P u n e - 4 1 1 0 0 5 . A v a i l a b l e f r o m : https://www.coep.org.in/resources/coeppal mprintdatabase. [Accessed: 23/09/2022].

Erkaymaz, O. (2020). Resilient Back-Propagation Approach in Small-Word Feed-Forward Neural Network Topology based on Newman-Watts Algorithm. Neural Computing and Applications, 30(20), 16279-16289.

Febriadi, B., Zamzami, Z., Yunefri, Y. and Wanto, A. (2018). Bipolar Function in Backpropagation Algorithm in Predicting Indonesia's Coal Exports by Major Destination Countries. IOP Conference Series: Materials Science and Engineering, 420(12089), 1–9.

Frigchholz, R.W. and Pieckmann, U. (2000). BioID: A Multimodal Biometric Identification System. Computer, 33(2), 64-68.

Ginantra, N.L.W.S.R., Bhawika, G.W., Daengs, G.A., Panjaitan, P.D., Arifin, M.A., Wanto, A., Amin, M., Okprana, H., Syafii, A. and Anwar, U. (2021). Performance One-step Secant Training Method for Forecasting Cases. Journal of Physics: Conference Series, 1933(1), 1-8.

Haidan, S.A., Rehman, Y. and Ali, S.M. (2020). Enhanced Multimodal Biometric Recognition based upon Intrinsic Hand Biometrics. Electronics, 9(11), 1-20.

He, C., Ma, M. and Wang, P. (2020). Extract Interpretability-Accuracy Balanced Rules from Artificial Neural Networks: A Review. Neurocomputing, 387, 346-358.

Hezil, H., Djemili, R. and Bourouba, H. (2018). Signature Recognition using Binary Features and KNN. International Journal of Biometrics, 10(1), 1-15.

Hu, S., Li, M., Wang, Q., Chow, S.S. and Du, M. (2018). Outsourced Biometrics Identification with Privacy. IEEE Transactions on Information Forensics and Security, 13(10), 2448-2463.

Huang, D. and Wu, Z. (2017). Forecasting Outpatient Visits using Empirical Mode Decomposition Coupled with Backpropagation Artificial Neural Networks Optimized by Particle Swarm Optimization. PLoS ONE, 12(2), 1–17.

Iula, A. and Micucci, M. (2022). Multimodal Biometric Recognition based on 3D Ultrasonic Palm Print- Hand Geometry Fusion. IEEE Access, 10, 7914-7925.

Jain, A., Hang, L. and Pankauti, S (2000). Biometric Identification. Communication of the ACM, 43(2), 90-98.

Khurshid, M. and Selwal, A. (2020). A Novel Block Hashing-based Template Security Scheme for Multimodal Biometric System. In: Kapur, P., Singh, G., Klochkov, Y., Kumar, U. (eds), Decision Analytics Applications in Industry. Asset Analytics. Springer, Singapore, pp. 173-183.

Lillicrap, T.P., Santoro, A., Marris, L., Akerman, C.J. and Hinton, G. (2020). Backpropagation and the Brain. Nature Reviews Neuroscience, 21(6), 335-346.

Liu, Q., Sang, R. and Zhang, Q. (2016). FPGA-based Acceleration of Davidon-Fletcher-Powell Quasi-Newton Optimization Method. Transaction of Tianjin University, 22(5), 381-387.

Nguyen, K., Fookes, C., Jillela, R., Sridharan, S. and Ross, A. (2017). Long Range Iris Recognition: A Survey. Pattern Recognition, 72, 123-143.

Nguyen, Q.H., Ly, H.B., Tran, V.Q., Nguyen, T.A., Phan, V.H., Le, T.T. and Pham, B.T. (2020). ANovel Hybrid Model based on a Feedforward Neural Network and One Step Secant Algorithm for Prediction of Load-Bearing Capacity of Rectangular Concrete-Filled Steel Tube Columns. Molecules, 25(15), 1-26.

Piciucco, E., Maiorana, E. and Campisi, P. (2018). Palm Vein Recognition using a High Dynamic Range Approach. IET Biometrics, 7(5), 439-446.

Prabu, S., Lakshmanan, M. and Noor Mohammed, V. (2019). A Multimodal Authentication for Biometric Recognition System using Intelligent Hybrid Fusion Techniques. Journal of Medical Systems, 43(8), 1-9.

Sancen-Plaza, A., Contreras-Medina, L.M., Barranco-Gutiérrez, A.I., Villaseñor-Mora, C., Martínez-Nolasco, J.J. and Padilla-Medina, J.A. (2020). Facial Recognition for Drunk People using Thermal Imaging. Mathematical Problems in Engineering, 2020, 1-9.

Sarfaz, N. (2019). Adermatoglyphia: Barriers to Biometric Identification and the Need for Standardized Alternative. Cureus, 11(2), 1-9.

Sepas-Moghaddam, A., Pereira, F.M. and Correia, P.L. (2020). Face Recognition: A Novel Multi-Level Taxonomy based Survey. IET Biometrics, 9(2), 58-67.

Setti, S. and Wanto, A. (2019). Analysis of Backpropagation Algorithm in Predicting the Most Number of Internet Users in the World. Journal Online Informatika, 3(2), 110-115.

Silaban, H., Zarlis, M. and Sawaluddin, S. (2017). Analysis of Accuracy and Epoch on Back-Propagation BFGS Quasi-Newton. Journal of Physics: Conference Series, 930, 1-5.

Solikhun, M.W., Safii, M. and Zarlis, M. (2020). Backpropagation Network Optimization Using One Step Secant (OSS) Algorithm. In IOP Conference Series Material Science and Engineering, 769(1), 1-11.

Upadhyay, D. (2013). Classification of EEG Signals under Different Mental Tasks using Wavelet Transform and Neural Network with One Step Secant Algorithm. International Journal of Scientific Engineering and Technology, 2(4), 256-259.

Velmuruson, S. and Selverajon, S. (2019). A Multimodal Authentication for Biometric Recognition System using Hybrid Fusion Technique. Cluster Computing, 22(6), 13429-13436.

Wang, M., Hu, J. and Abbass, H.A. (2020). BrainPrint: EEG Biometric Identification based on Analyzing Brain Connectivity Graphs. Pattern Recognition, 105, 1-13.

Wanto, A., Windarto, A.P., Hartama, D. and Parlina, I. (2017). Use of Binary Sigmoid Function and Linear Identity in Artificial Neural Networks for Forecasting Population Density. International Journal of Information System & Technology, 1(1), 43–54.

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2026-09-09
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How to Cite

POTENTIALS OF ONE STEP SECANT AND FLETCHER-POWELL CONJUGATE GRADIENT BACK PROPAGATION ALGORITHMS FOR MULTIMODAL BIOMETRIC RECOGNITION APPLICATIONS. (2026). FUTA JOURNAL OF ENGINEERING AND ENGINEERING TECHNOLOGY, 16(2), 73-81. https://doi.org/10.51459/futajeet.2022.16.2.700

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