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%

Approaching real time dynamic signature verification from a systems and control perspective.

Student Number : 9901877H MSc Dissertation School of Electrical and Information Engineering Faculty of Engineering and the Built Environmentalgorithm. The origins of handwriting idiosyncrasies and habituation are explained using systems theory, and it is shown that the 2/3 power law governing biomechanics motion also applies to handwriting. This leads to the conclusion that it is possible to derive handwriting velocity profiles from a static image, and that a successful forgery of a signature is only possible in the event of the forger being able to generate a signature using natural ballistic motion. It is also shown that significant portion of the underlying dynamic system governing the generation of handwritten signatures can be inferred by deriving time segmented transfer function models of the x and y co-ordinate velocity profiles of a signature. The prototype algorithm consequently developed uses x and y components of pen-tip velocity profiles (vx[n] and vy[n]) to create signature representations based on autoregression-with-exogenous-input (ARX) models. Verification is accomplished using a similarity measure based on the results of a k-step ahead predictor and 5 complementary metrics. Using 350 signatures collected from 21 signers, the system’s false acceptance (FAR) and false rejection (FRR) rates were 2.19% and 27.05% respectively. This high FRR is a result of measurement inadequacies, and it is believed that the algorithm’s FRR is approximately 18%