TY - JOUR
T1 - Wavelet-based off-line handwritten signature verification
AU - Deng, Peter Shaohua
AU - Liao, Hong Yuan Mark
AU - Ho, Chin Wen
AU - Tyan, Hsiao Rong
N1 - Funding Information:
This work was partially supported by the National Science Council of Taiwan under Grant NSC86-2745-E-001-004.
PY - 1999/12
Y1 - 1999/12
N2 - In this paper, a wavelet-based off-line handwritten signature verification system is proposed. The proposed system can automatically identify useful and common features which consistently exist within different signatures of the same person and, based on these features, verify whether a signature is a forgery or not. The system starts with a closed-contour tracing algorithm. The curvature data of the traced closed contours are decomposed into multiresolutional signals using wavelet transforms. Then the zero-crossings corresponding to the curvature data are extracted as features for matching. Moreover, a statistical measurement is devised to decide systematically which closed contours and their associated frequency data of a writer are most stable and discriminating. Based on these data, the optimal threshold value which controls the accuracy of the feature extraction process is calculated. The proposed approach can be applied to both on-line and off-line signature verification systems. Experimental results show that the average success rates for English signatures and Chinese signatures are 92.57% and 93.68%, respectively.
AB - In this paper, a wavelet-based off-line handwritten signature verification system is proposed. The proposed system can automatically identify useful and common features which consistently exist within different signatures of the same person and, based on these features, verify whether a signature is a forgery or not. The system starts with a closed-contour tracing algorithm. The curvature data of the traced closed contours are decomposed into multiresolutional signals using wavelet transforms. Then the zero-crossings corresponding to the curvature data are extracted as features for matching. Moreover, a statistical measurement is devised to decide systematically which closed contours and their associated frequency data of a writer are most stable and discriminating. Based on these data, the optimal threshold value which controls the accuracy of the feature extraction process is calculated. The proposed approach can be applied to both on-line and off-line signature verification systems. Experimental results show that the average success rates for English signatures and Chinese signatures are 92.57% and 93.68%, respectively.
UR - http://www.scopus.com/inward/record.url?scp=0033356975&partnerID=8YFLogxK
U2 - 10.1006/cviu.1999.0799
DO - 10.1006/cviu.1999.0799
M3 - 期刊論文
AN - SCOPUS:0033356975
VL - 76
SP - 173
EP - 190
JO - Computer Vision and Image Understanding
JF - Computer Vision and Image Understanding
SN - 1077-3142
IS - 3
ER -