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 Convolution 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). The main purpose of this research is to develop a complete system of face recognition. 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.
Highlights
A day, people are using combinations of alphabets and numbers as their secret code to access their account
The third part is for convolutional neural network (CNN) training
The user ID should be identical with the ID label attached for each subject
Summary
People are using combinations of alphabets and numbers as their secret code to access their account. Biometrics is a method to identify individuals by unique biological traits that individual possesses such as features of face, finger print and finger vein, iris, blood type, DNA and many more. Recognition using these biometric traits provides high security since an account cannot be accessed by any other individuals without the presence of the account owner. Face recognition remains a challenging biometric problem since no technique could provide a robust solution to all situations such as variation of facial expression, pose invariant, illumination invariant, and occlusion among others. Biometric has a wide range application especially in surveillance, security monitoring, immigration, any other applications for identification and recognition[1]
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