Integrating RBF-based Neural Network Face Expression Recognition in Access System

Ch’ng Yau Yau, A.F. Kadmin, S.F. Abd. Gani, S.F. Abd. Gani, K.A.A. Aziz, R.A. Hamzah, R.A. Hamzah, A.Z. Jidin

Abstract


Biometric recognition system such as facial recognition system was widely developed over the past few years. Facial recognition system is commonly used in security system to allow user to protect their privilege. The normal security like key or password is no longer relevant as people prefer an easier and flexible way. Therefore, this paper presents a better and easier way of security system that can recognize the user successfully and give the matching percentage. By using Radial Basis Function Neural Network in MATLAB, a face recognition system can be created. The RBF system will be trained by data as reference, input image will undergo the same process and the data obtained will be used to match with the data in the RBF to obtain the matching percentage. A suitable matching percentage reference was chosen from this analysis as the minimum require matching to access the security system where error rate is one of the main concerns where it is the unwanted result that might occur. Different threshold number, spread value, and sizes of dimension also tested, the differences on the output matching result were observed. By using the microcontroller to control a relay to control the magnetic door lock, the system was able to successfully control the door lock.


Full Text:

PDF

References


Ijaz, Sidra, Munam Ali Shah, Abid Khan, and Mansoor Ahmed. "Smart cities: A survey on security concerns." International Journal of Advanced Computer Science and Applications 7, no. 2 (2016): 612-625.

Gong, Yicheng, Yongxiang Zhang, Shuangshuang Lan, and Huan Wang. "A comparative study of artificial neural networks, support vector machines and adaptive neuro fuzzy inference system for forecasting groundwater levels near Lake Okeechobee, Florida." Water resources management 30, no. 1 (2016): 375-391.

Litjens, Geert, Thijs Kooi, Babak Ehteshami Bejnordi, Arnaud Arindra Adiyoso Setio, Francesco Ciompi, Mohsen Ghafoorian, Jeroen Awm Van Der Laak, Bram Van Ginneken, and Clara I. Sánchez. "A survey on deep learning in medical image analysis." Medical image analysis 42 (2017): 60-88.

Walczak, Steven. "Artificial neural networks." In Advanced Methodologies and Technologies in Artificial Intelligence, Computer Simulation, and Human-Computer Interaction, pp. 40-53. IGI Global, 2019.

Yoo, Sung-Hoon, Sung-Kwun Oh, and Witold Pedrycz. "Optimized face recognition algorithm using radial basis function neural networks and its practical applications." Neural Networks 69 (2015): 111-125.

Agarwal, Vandana, and Surekha Bhanot. "Radial basis function neural network-based face recognition using firefly algorithm." Neural Computing and Applications 30, no. 8 (2018): 2643-2660.

Halali, Mohamad A., Vahid Azari, Milad Arabloo, Amir H. Mohammadi, and Alireza Bahadori. "Application of a radial basis function neural network to estimate pressure gradient in water–oil pipelines." Journal of the Taiwan Institute of Chemical Engineers 58 (2016): 189-202.

Aziz, K. A. A., M. H. Mustafa, N. M. Z. Hashim, N. R. M. Nuri, A. F. Kadmin, and A. Salleh. "Smart Android Wheelchair Controller Design." International Journal for Advance Research In Engineering and Technology (IJARET) 3, no. 3 (2015): 42-48.

Majdisova, Zuzana, and Vaclav Skala. "Radial basis function approximations: comparison and applications." Applied Mathematical Modelling 51 (2017): 728-743.

Kadmin, A. F., A. Z. Jidin, Abu Bakar, KA A. Aziz, and WN Abd Rashid. "Wireless Voice-Based Wheelchair Controller System." Journal of Telecommunication, Electronic and Computer Engineering (JTEC) 8, no. 7 (2016): 117-122.

Montazer, Gholam Ali, and Davar Giveki. "An improved radial basis function neural network for object image retrieval." Neurocomputing 168 (2015): 221-233.

Akpan, Vincent A., Joshua B. Agbogun, Michael T. Babalola, and Bamidele A. Oluwade. "Radial basis function neuroscaling algorithms for efficient facial image recognition." Mach. Learn. Res. 2, no. 4 (2017): 152-168.

Hamzah, Rostam Affendi, M. Saad Hamid, A. F. Kadmin, and S. Fakhar Abd Ghani. "Improvement of stereo corresponding algorithm based on sum of absolute differences and edge preserving filter." In 2017 IEEE International Conference on Signal and Image Processing Applications (ICSIPA), pp. 222-225. IEEE, 2017.

Santoro, Adam, David Raposo, David G. Barrett, Mateusz Malinowski, Razvan Pascanu, Peter Battaglia, and Timothy Lillicrap. "A simple neural network module for relational reasoning." In Advances in neural information processing systems, pp. 4967-4976. 2017.

Schroff, Florian, Dmitry Kalenichenko, and James Philbin. "Facenet: A unified embedding for face recognition and clustering." In Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 815-823. 2015.

Kadmin, A. F., K. A. A. Aziz, A. R. Soufhwee, SS Abd Razak, M. Z. Salehan, NA Abdul Hadi, R. A. Hamzah, and WN Abd Rashid. "Performance Analysis of Neural Network Model for Automated Visual Inspection with Robotic Arm Controller System." Journal of Telecommunication, Electronic and Computer Engineering (JTEC) 10, no. 2-2 (2018): 19-22.

Wechsler, Harry, Jonathon P. Phillips, Vicki Bruce, Françoise Fogelman Soulié, and Thomas S. Huang, eds. Face recognition: From theory to applications. Vol. 163. Springer Science & Business Media, 2012.


Refbacks

  • There are currently no refbacks.


ISSN : 2590-3551, eISSN : 2600-8122     

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0).

Best viewed using Mozilla Firefox, Google Chrome and Internet Explorer with the resolution of 1280 x 800