3,454 research outputs found
Signature Verification using Normalized Static Features and Neural Network Classification
Signature verification is very widely used in verification of the identity of any person. Now a days other biometric verification system has been evolved very widely like figure print, iris etc., but signature verification through computer system is still in development phase. The verification system is either through offline mode or online mode in online systems the dynamic information of a signature captured at the time the signature is made while in offline systems based on the scanned image of a signature. In this paper, a method is presented for Offline signatures Verification, for this verification system signature image is first pre-processed and converted into binary image of same size with 200x200 Pixels and then different features are extracted from the image like Eccentricity, Kurtosis, Skewness etc. and that features are used to train the neural network using back-propagation technique. For this verification system 6 different user signatures are taken to make database of the feature and results are analysed. The result demonstrate the efficiency of the proposed methodology when compared with other existing studies. The proposed algorithm gives False Acceptance Rate (FAR) as 5.05% and False Rejection rate (FRR) as 4.25%
Off-line Signature Verification Based on Fusion of Grid and Global Features Using Neural Networks
Signature is widely used and developed area of research for personal verification and authentication. In this paper Off-line Signature Verification Based on Fusion of Grid and Global Features Using Neural Networks (SVFGNN) is presented. The global and grid features are fused to generate set of features for the verification of signature. The test signature is compared with data base signatures based on the set of features and match/non match of signatures is decided with the help of Neural Network. The performance analysis is conducted on random, unskilled and skilled signature forgeries along with genuine signatures. It is observed that FAR and FRR results are improved in the proposed method compared to the existing algorithm
Signature Pattern Recognition using Kohonen Network
A signature is a special form of handwriting that used for human identification process. The current identification process is extremely ineffective. People have to manually compare signatures with the previously stored data. This study proposed SOM Kohonen algorithm as the method of signature pattern recognition. This method has able to visualize high-dimensional data. The image processing method is used in this study in pre-processing data phase. The accuracy of SOM Kohonen was 70 %, indicated the method used was good enough for pattern recognition
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%
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