By Mario Köppen (auth.), Hiroshi Sako, Katrin Y. Franke, Shuji Saitoh (eds.)
This e-book 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 awarded including invited keynote papers have been rigorously chosen in the course of rounds of reviewing and revision. The papers conceal a variety of present themes in computational forensics together with authentication, biometrics, record research, multimedia, forensic software assessment, personality reputation, 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
Sample text
The system of this paper is evaluated in three different scenarios for identity modeling which exploit to a different degree the class information provided by the manual segmentation of alphanumeric samples: i) modeling per individual sample, ii) modeling per alphanumeric channel, and iii) modeling per writer. e. does not use the character class information). Results show that much better performance is obtained by using channel information, justifying the considerable amount of time spent by the trained operator in the segmentation and labeling process.
Similarly, we define the reference pattern as B = B1B 2 . . Bl . . BN . We define δ(k, l) = D(Ak , B l ) as a stroke distance between input stroke Ak and reference stroke B l . Note that the dimensionality of Ak and B l are often different due to the difference of their stroke lengths. Thus, we cannot calculate the simple Euclidean distance between them. Instead, DP-matching distance [8] has been utilized for calculating a distance between a pair of strokes with different lengths. 46 W. Cai, S. Uchida, and H.
Alonso-Fernandez et al. results in 62×30×30=55,800 computed distances. 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.