TY  - JOUR
TI  - An Offline Handwritten Signature Verification Using Low Level Stroke with Feature Extraction and Hybrid Classifiers
AU  - Nikhil Gupta
AU  -  Dr. Rakesh Dhiman
JO  - International Journal of Scientific Research in Computer Science, Engineering and Information Technology
PB  - Technoscience Academy
DA  - 2017/12/31
PY  - 2017
DO  - https://doi.org/10.32628/IJSRCSEIT
UR  - https://ijsrcseit.com/CSEIT1726283
VL  - 2
IS  - 6
SP  - 1055	
EP  - 1061
AB  - Biometrics can be classified into two types, namely, physiological (fingerprint, Iris, face recognition etc) and behavioural (signature verification, keystroke dynamics etc.). In an authentication system, signature identification and verification plays an important role. Signature identification is again classified into two types, that is, static signature recognition (offline) and dynamic signature recognition (online).Online signature verification system uses a special sensor for capturing the image whereas in offline signature identification, no special sensor is required. Offline signature system needs only a pen and paper. Signature authentication is accepted as a legal mark of identification and authorization and finds an application in different fields like finance, bank and in jurisdictional documents. In this research work, we have proposed an offline signature verification system. Signature verification is a process in which a genuine person has been recognized on the basis of their signature. In the proposed work, signatures are executed by three processes like pre-processing, feature extraction and classification. In pre-processing, Binarization and color conversion has been performed. For extracting features, Low-level stroke feature technique along with SIFT method has been used. In the proposed work, we have used the combination of SVM and ANN as a classifier to classify the test data according to the training set. Initially, the features are trained using SVM, after that, the output of SVM act as the input of ANN and creates a better training structure to achieve better accuracy of proposed signature recognition system. The simulation is being performed in image processing toolbox under the MATLAB software. The performance metrics like FAR, FRR and Accuracy has been measured and comparison of proposed with existing technique has been provided.