Download e-book for kindle: Computational Forensics: 4th International Workshop, IWCF by Mario Köppen (auth.), Hiroshi Sako, Katrin Y. Franke, Shuji

By Mario Köppen (auth.), Hiroshi Sako, Katrin Y. Franke, Shuji Saitoh (eds.)

ISBN-10: 3642193757

ISBN-13: 9783642193750

This publication constitutes the completely refereed post-proceedings of the 4th foreign Workshop on Computational Forensics, IWCF 2010, held in Tokyo, Japan in November 2010. The sixteen revised complete papers provided including invited keynote papers have been rigorously chosen in the course of rounds of reviewing and revision. The papers hide quite a lot of present issues in computational forensics together with authentication, biometrics, rfile research, multimedia, forensic device review, personality acceptance, and forensic verification.

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Additional info for Computational Forensics: 4th International Workshop, IWCF 2010, Tokyo, Japan, November 11-12, 2010, Revised Selected Papers

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K. ) ICBA 2004. LNCS, vol. 3072, pp. 555–560. Springer, Heidelberg (2004) 11. , Ross, A. ): Handbook of Biometrics. Springer, Heidelberg (2008) 12. : A threshold selection method for gray-level histograms. IEEE Trans. on Systems, Man and Cybernetics 9, 62–66 (1979) 13. : Digital Image Processing. Addison-Wesley, Reading (2002) 14. : Score Normalization in Multimodal Biometric Systems. Pattern Recognition 38(12), 2270–2285 (2005) 15. : Adapted user-dependent multimodal biometric authentication exploiting general information.

E. stroke distances) of matching edges is minimum. Hsieh et al. [5] formulated this minimization problem by the following LP: minimize subject to N k=1 N k=1 N l=1 δ(k, l)xkl xkl = 1, ∀l, N l=1 Note that xkl = 1 when Ak is matched with B l . xkl = 1, ∀k, xkl ∈ {0, 1}. Toward Forensics by Stroke Order Variation 49 Table 1. 0 ∗ Mark “ ” denotes the stroke correspondence to be determined by SM. Fig. 6. Finding the correspondence between input strokes and reference strokes by SM (N = 4) They applied the Hungarian method [9,10,11], the well-known primal-dual algorithm, to solve this LP problem.

In case of writers having the same number of winning channels, we apply the same above criteria. 3. Modeling per writer (channel independent). This case computes a unique probability distribution per writer by combining all the available samples of all the alphanumeric characters. In this case, we do not use the character class information, obtaining a unique writing identity model that averages information from the 62 channels. Only one distance between two writers is obtained, which is used for identification.

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Computational Forensics: 4th International Workshop, IWCF 2010, Tokyo, Japan, November 11-12, 2010, Revised Selected Papers by Mario Köppen (auth.), Hiroshi Sako, Katrin Y. Franke, Shuji Saitoh (eds.)


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