A Biometric System for Personal Identification Using Modular Neural Nets.
El-Bakry, H., Abo-Elsoud, M., Kamel, M. (2021). A Biometric System for Personal Identification Using Modular Neural Nets.. EKB Journal Management System, 25(1), 30-38. doi: 10.21608/bfemu.2021.146280
Hazem M. El-Bakry; Mohy A. Abo-Elsoud; Mohamed S. Kamel. "A Biometric System for Personal Identification Using Modular Neural Nets.". EKB Journal Management System, 25, 1, 2021, 30-38. doi: 10.21608/bfemu.2021.146280
El-Bakry, H., Abo-Elsoud, M., Kamel, M. (2021). 'A Biometric System for Personal Identification Using Modular Neural Nets.', EKB Journal Management System, 25(1), pp. 30-38. doi: 10.21608/bfemu.2021.146280
El-Bakry, H., Abo-Elsoud, M., Kamel, M. A Biometric System for Personal Identification Using Modular Neural Nets.. EKB Journal Management System, 2021; 25(1): 30-38. doi: 10.21608/bfemu.2021.146280

A Biometric System for Personal Identification Using Modular Neural Nets.

MEJ- Mansoura Engineering Journal
Article 5, Volume 25, Issue 1, March 2000, Page 30-38  XML PDF (212.27 K)
Document Type: Research Studies
DOI: 10.21608/bfemu.2021.146280
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Authors
Hazem M. El-Bakry* 1; Mohy A. Abo-Elsoud2; Mohamed S. Kamel3
1Electrical Communication Department., Faculty of Computer Science Electronics & Information System., El-Mansoura University., Mansoura., Egypt.
2Electronics & Electrical Communication Department., Faculty of Engineering., El-Mansoura University., Mansoura., Egypt.
3Systems Design Engineering Departremnt., University of Waterloo., Canada
Abstract
 In this paper, a fast biometric system for face recognition is introduced. We combine both fast and cooperative modular neural nets (MNNs) to enhance the performance of the detection process Such approach is applied to identify frontal views of human faces automatically in cluttered scenes. In the detection phase. neural nets are used to test whether a window of 20x20 pixels contains a face or not The large number of examples required for face and nonface images makes the convergence process very difficult during the learning process. A simple design for cooperative modular neural nets is presented to solve this problem by dividing these data into three groups Such division results in reduction of computational complexity and thus decreasing the time and memory needed during the test of an image. For the recognition phase, feature measurements are made through Fourier descriptors which are insensitive to rotation, translation and scaling Such feature is modified to reduce the number of neurons in the hidden layer. From these features, wavelet coefficients are extracted which have been shown to provide advantages in terms of better representation for a given data to be compressed finally, the resulted vector is fed to a neural net for face classification. Simulation results for the proposed algorithm show a good performance.
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