A Signature Identification Method Based on Strength and Strokes Direction

Abstract:

It was much more complex and difficult for off-line signature identification attributable to the limitation of available information. To solve the problem, a signature identification method based on strength and strokes direction was proposed. The signature image acquired was gray-scaled and filtered at the stage of preprocess; then the image was two-valued with different threshold based on strength feature, the regions which grayscale was less than threshold were retained; the strokes which possess distinctive directional feature were extracted by using mathematical morphology and combining different scales/directions structure element based on strokes direction feature; at last judgement was maked for sample in accordance with corresponding feature. Experimental results showed the proposed method can enhance accurate rate effectively, improve real-time performance, which was a try beneficial to apply new methods for off-line signature identification.

• Periodical: Advanced Engineering Forum (Volumes 2-3)
• Pages: 223-227
• DOI: https://doi.org/10.4028/www.scientific.net/AEF.2-3.223
• Online since: December 2011
• Authors: Hai Long Huang, Hong Wang, Li Ji
• Keywords: Mathematical Morphology, Off-Line Signature Identification, Preprocess, Real-Time, Structure Element

References

[1] Li Xin, Ding Xiao-qing. Writer identification based on improved microstructure features [D]. J Tsing hua Univ ( Sci & Tech), 2010, 50(4)：595-600

[2] Plamondon R, Lorette G. Automatic signature verification and writer identification—The state of the art[J]. Pattern Recognition, 1989, 22(2)：107-131

DOI: 10.1016/0031-3203(89)90059-9

[3] Said H E S, TAN Tieniu, Baker K D. Personal identification based on handwriting[J]. Pattern Recognition, 2000, 33(1)：149-160

DOI: 10.1016/s0031-3203(99)00006-0

[4] Bulacu M, Schomaker L. Text independent writer identification and verification using textural and allographic features[J]. IEEE Trans Pattern Analysis and Machine Intelligence, 2007, 29(2)：701-717

DOI: 10.1109/tpami.2007.1009

[5] Wang Hong, Zhao Hai-bin. Digital image processing -JAVA language implementation [M]. Shenyang：Northeastern University Press, 2005：158-166

[6] Jing Xiao-jun. Image processing technology and application [M]. Beijing：National Defense Industry Press, 2005：211-216

[7] Wang Jia-wen. Matlab7.6 graphics image processing[M]. Beijing：National Defense Industry Press, 2009：264-291

[8] Lee C K, Wong S P. A mathematical morphological approach for segmenting heavily noise-corrupted images [J]. Pattern Recognition, 1996, 29(8)：1347-1358

DOI: 10.1016/0031-3203(96)86888-9

[9] Naegel B. Using mathematical morphology for the anatomical labeling of vertebrae from 3D CT-scan images[J]. Computerized Medical Imaging and Graphics, 2007, 3：141-156

DOI: 10.1016/j.compmedimag.2006.12.001

[10] Maria C M. Fuzzy mathematical morphology：concepts and applications [J]. Vistas in Astronomy, 1996, 40(4)：469-47