Writer identification is an effective solution to personal identification, which is necessary in many commercial and governmental sections of human society. In spite of continuous effort, writer identification, especially the off-line, textindependent writer identification, still remains as a challenging problem. In this paper, we propose a new method, which combines the wavelet theory and statistical model (more accurately, generalized Gaussian density (GGD) model), for off-line, text-independent writer identification. This method is based on our discovery that wavelet coefficients within each high-frequency subband of the handwritings satisfy GGD distribution. For different handwritings, the GGD parameters vary and thus can be selected as the handwriting features. Our experiments show this novel method, compared with two-dimensional Gabor model, one classic method on off- line, text-independent writer identification, not only achieves much better identifi- cation results but also greatly reduces the elapsed time on calculation.
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References
A.K. Jain. Recent development on biometric authentication. In Proceeedings of Advanced Study Institute (ASI). Hong Kong Baptist University, Hong Kong, 2004
M. Benecke. DNA typing in forensic medicine and in criminal investigations: A current survey. Natur Wissenschaften, 84(5):181–188, 1997
B. Devlin, N. Risch, and K. Roeder. Forensic inference from DNA fingerprints. Journal of American Statistical Association, 87(418):337–350, 1992
J. Daugman. The importance of being random: Statistical principles of iris recognition. Pattern Recognition, 36(2):279–291, 2003
A. Jain, L. Hong, and R. Bolle. On-line fingerprint verification. IEEE Transactions on Pattern Analysis and Machine Intelligence, 19(4):302–314, 1997
S. Srihari, S. Cha, H. Arora, and S. Lee. Individuality of handwriting. Journal of Forensic Sciences, 47(4):1–17, 2002
H.E.S. Said, T. Tan, and K. Baker. Writer identification based on handwriting. Pattern Recognition, 33(1):133–148, 2000
Y. Zhu, T. Tan, and Y. Wang. Biometric personal identification based on handwriting. In Proceedings of the 15th International Conference on Pattern Recognition, pages 801–804, 2000
R. Plamondon and G. Lorrtte. Automatic signature vertification and writer idenfication idenfication – the state of the art. Pattern Recognition, 37(12):107–131, 1989
E.N. Zois. Morphological wavelform coding for writer identication. Pattern Recognition, 33:385–398, 2000
C. Hertel and H. Bunke. A set of novel features for writer identification. In AVBPA, pages 679–687, 2003
A. Schlapbach and H. Bunke. Off-line handwriting identification using HMM based recognizers. In Proceedings of 17th International Conference on Pattern Recognition, volume 2, pages 654–658, 2004
M. Bulacu, L. Schomarker, and L. Vuurpijl. Writer identification using edge-based directional features. In Proceedings of the 7th International Conference on Document Analysis and Recognition, pages 937–941, 2003
T.A. Nosary and L. Heutte. Definiting writer’s invariants to adapt the recognition task. In Proceedings of the 5th International Conference on Document Analysis and Recognition, volume 22, no. 1, pages 765–768, 1999
A. Bensefia, A. Nosary, T. Paquet, and L. Heutte. Writer Idenfication by writer’s invariants. In Proceedings of the 8th International Workshop on Frontiers in Handwriting Recognition, pages 274–279, 2002
Z. He. Writer identification using wavelet, contourlet and statistical models. Ph.D Thesis. Hong Kong Baptist University, 2006
I. Daubechies. Ten Lectures on wavelets. SIAM, 1992
E.P. Simoncelli. Handbook of Video and Image Processing, 2nd edn. Academic, USA, 2005
M.N. Do and M. Vetterli. Wavelet-based texture retrieval using generalized gaussian density and Kullback–Leibler distance. IEEE Transactions on Image Processing, 11:146–158, 2002
O. Commowick, C. Lenglet, and C. Louchet. Wavelet-based Texture Classification and Retrieval. Technical Report, http://www.tsi.enst.fr/fsi/enseignement/ressources/mti/ReportFinal.html, 2003
T. Chang and C.C.J. Kuo. Texture analysis and classfication with tree-structure wavelet transform. IEEE Transactions on Image Processing, 2(4):429–441, 1985
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He, Z., Tang, Y.Y. (2008). A Wavelet-based Statistical Method for Chinese Writer Identification. In: Bunke, H., Kandel, A., Last, M. (eds) Applied Pattern Recognition. Studies in Computational Intelligence, vol 91. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-76831-9_8
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