Signature Recognition System for Student Attendance System in UTP

This paper proposes an off-line signature recognition system for student attendance system in Universiti Teknologi PETRONAS (UTP). In current system, attendance sheet is passed across the class and students are required to signed on the paper. Later, lecturers will check on the paper and mark any empty column. However, lecturers always busy and seldom have time to check each signature. Basically, the system has the ability to imitate humans' capability of recognizing signatures. Thus, it could help lecturers in recognizing students' signatures. The system employs artificial neural networks for recognition and training process. This system is developed mainly using Visual Basic 6.0 and involves four basic steps, which are image acquisition, image pre processing, and enrolment and verification process. It has two phases, training and recognition. Both process use artificial neural network. The system was satisfactory in all cases where there were two different signatures to be recognized with False Rejection Rate (FRR) for genuine signature is 4% and False Acceptance Rate (FAR) for forged signature is 28%