Writer Identification for Smart Meeting Room Systems
Conference paper
Abstract
In this paper we present a text independent on-line writer identification system based on Gaussian Mixture Models (GMMs). This system has been developed in the context of research on Smart Meeting Rooms. The GMMs in our system are trained using two sets of features extracted from a text line. The first feature set is similar to feature sets used in signature verification systems before. It consists of information gathered for each recorded point of the handwriting, while the second feature set contains features extracted from each stroke. While both feature sets perform very favorably, the stroke-based feature set outperforms the point-based feature set in our experiments. We achieve a writer identification rate of 100% for writer sets with up to 100 writers. Increasing the number of writers to 200, the identification rate decreases to 94.75%.
Keywords
Gaussian Mixture Model Text Line Universal Background Model Handwritten Text Writer Identification
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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References
- 1.Collobert, R., Bengio, S., Mariéthoz, J.: Torch: a modular machine learning software library. Technical report, IDIAP (2002)Google Scholar
- 2.Czyz, J., Bengio, S., Marcel, C., Vandendorpe, L.: Scalability analysis of audio-visual person identity verification. In: Audio- and Video-based Biometric Person Authentication, pp. 752–760 (2003)Google Scholar
- 3.Dempster, A.P., Laird, N.M., Rubin, D.B.: Maximum likelihood from incomplete data via the EM algorithm. Journal of Royal Statistical Society 39, 1–38 (1977)zbMATHMathSciNetGoogle Scholar
- 4.Fasel, B., Luettin, J.: Automatic facial expression analysis: A survey. Pattern Recognition 36, 259–275 (2003)zbMATHCrossRefGoogle Scholar
- 5.Grudin, M.A.: On internal representations in face recognition systems. Pattern Recognition 33, 1161–1177 (2000)CrossRefGoogle Scholar
- 6.Jaeger, S., Manke, S., Reichert, J., Waibel, A.: Online handwriting recognition: the NPen++ recognizer. Int. Journal on Document Analysis and Recognition 3, 169–180 (2001)CrossRefGoogle Scholar
- 7.Jain, A., Griess, F., Connell, S.: On-line signature verification. Pattern Recognition 35, 2663–2972 (2002)Google Scholar
- 8.Johansson, S.: The tagged LOB Corpus: User’s Manual. Norwegian Computing Centre for the Humanities, Norway (1986)Google Scholar
- 9.Liwicki, M., Bunke, H.: Handwriting recognition of whiteboard notes. In: Proc. 12th Conf. of the Int. Graphonomics Society, pp. 118–122 (2005)Google Scholar
- 10.Liwicki, M., Bunke, H.: IAM-OnDB – an on-line English sentence database acquired from handwritten text on a whiteboard. In: 8th Int. Conf. on Document Analysis and Recognition (2005) (Accepted for publication)Google Scholar
- 11.Mariéthoz, J., Bengio, S.: A comparative study of adaptation methods for speaker verification. In: Int. Conf. on Spoken Language Processing, Denver, CO, USA, pp. 581–584 (2002)Google Scholar
- 12.McCowan, L., Gatica-Perez, D., Bengio, S., Lathoud, G., Barnard, M., Zhang, D.: Automatic analysis of multimodal group actions in meetings. IEEE Trans. on Pattern Analysis and Machine Intelligence 27, 305–317 (2005)CrossRefGoogle Scholar
- 13.Moore, D.: The IDIAP smart meeting room. Technical report, IDIAP-Com (2002)Google Scholar
- 14.Morgan, N., Baron, D., Edwards, J., Ellis, D., Gelbart, D., Janin, A., Pfau, T., Shriberg, E., Stolcke, A.: The meeting project at ICSI. In: Proc. Human Language Technologies Conf., pp. 246–252 (2001)Google Scholar
- 15.Reiter, S., Rigoll, G.: Segmentation and classification of meeting events using multiple classifier fusion and dynamic programming. In: Proc. 17th Int. Conf. on Pattern Recognition, pp. 434–437 (2004)Google Scholar
- 16.Reynolds, D.A., Quatieri, T.F., Dunn, R.B.: Speaker verification using adapted Gaussian mixture models. Digital Signal Processing 10, 19–41 (2000)CrossRefGoogle Scholar
- 17.Richiardi, J., Drygajlo, A.: Gaussian Mixture Models for on-line signature verification. In: Proc. 2003 ACM SIGMM workshop on Biometrics methods and applications, pp. 115–122 (2003)Google Scholar
- 18.Sanderson, C., Paliwal, K.K.: Fast features for face authentication under illumination direction changes. Pattern Recognition Letters 24, 2409–2419 (2003)CrossRefGoogle Scholar
- 19.Schenkel, M., Guyon, I., Henderson, D.: On-line cursive script recognition using time delay neural networks and hidden Markov models. Machine Vision and Applications 8, 215–223 (1995)CrossRefGoogle Scholar
- 20.Schlapbach, A., Bunke, H.: Off-line handwriting identification using HMM based recognizers. In: Proc. 17th Int. Conf. on Pattern Recognition, vol. 2, pp. 654–658 (2004)Google Scholar
- 21.Schomaker, L.: From handwriting analysis to pen-computer applications. IEE Electronics & Communication Engineering Journal 10, 93–102 (1998)CrossRefGoogle Scholar
- 22.Waibel, A., Schultz, T., Bett, M., Malkin, R., Rogina, I., Stiefelhagen, R., Yang, J.: SMaRT: The Smart Meeting Room Task at ISL. In: Proc. IEEE Int. Conf. on Acoustics, Speech, and Signal Processing, vol. 4, pp. 752–755 (2003)Google Scholar
- 23.Wellner, P., Flynn, M., Guillemot, M.: Browsing recorded meetings with Ferret. In: Machine Learning for Multimodal Interaction, pp. 12–21 (2004)Google Scholar
- 24.Yamazaki, Y., Nagao, T., Komatsu, N.: Text-indicated writer verification using hidden Markov models. In: Proc. 7th Int. Conf. on Document Analysis and Recognition, pp. 329–332 (2003)Google Scholar
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