Affiliation

Faculty of Electronic and Computer Engineering, Universiti Teknikal Malaysia Malacca, Malaysia

Corresponding Author

Syafeeza, A.R., Faculty of Electronic and Computer Engineering, Universiti Teknikal Malaysia Malacca, Malaysia, E-mail: syafeeza@utem.edu.my

Citation

Syafeeza, A.R., et al. A MATLAB-Based Convolutional Neural Network Approach for Face Recognition System (2016) Bioinfo Proteom Img Anal 2(1): 71-75 .

Copy rights

© 2016 Syafeeza, A.R. This is an Open access article distributed under the terms of Creative Commons Attribution 4.0 International License.

Keywords

Abstract

The research on face recognition still continues after several decades since the study of this biometric trait exists. This paper discusses a method on developing a MATLAB-based Convolutional Neural Network (CNN) face recognition system with Graphical User Interface (GUI) as the user input. The proposed CNN has the ability to accept new subjects by training the last two layers out of four layers to reduce the neural network training time. The image preprocessing steps were implemented in MATLAB, while the CNN algorithm was implemented in C language (using GCC compiler). A Graphical User Interface (GUI) in MATLAB links all the steps starting from image preprocessing to face identification process. Evaluation was carried out using the images of 40 subjects from AT&T database and 10 subjects from JAFFE database producing 100% accuracy with less than 1 minute average training time when inserting 1 to 10 new subjects into the system.