Fundamentals of Sketch-Based Passwords pp 17-33 | Cite as

# Sketch-Based Authentication

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## Abstract

This chapter considers two different recognition/matching algorithms, namely dynamic time warping (DTW) and Simple K-Space (SKS). Both algorithms utilize classical pattern recognition techniques to the novel application of sketch-based password. While more sophisticated techniques are possible, DTW and SKS are chosen because the inherent nature of the underlying problem is easily perceived using the approaches. Plus, the fundamental concepts discussed in the chapter generalize beyond the specific algorithmic implementation. The overall sketch-based authentication framework is outlined in this chapter using DTW and SKS. The respective implementation details are rigorously discussed, from which the philosophies are compared and contrasted. In particular, these methods have a similar objective, but different parameterizations, operating spaces, and computational complexities.

## Keywords

DTW SKS Correspondence Density estimation Sketch Authentication## References

- 1.D. Ballard. Generalizing the Hough Transform to Detect Arbitrary Shapes.
*Pattern Recognition*, 13(2):111–122, 1981.CrossRefzbMATHGoogle Scholar - 2.R. Bellman.
*Dynamic Programming*. Princton University Press, 1957.Google Scholar - 3.R. O. Duda, P. E. Hart, and D. G. Stork.
*Pattern Classification*. John Wiley & Sons, 2nd edition, 2001.Google Scholar - 4.J. Harris and H. Stocker.
*Handbook of mathematics and computational science*. Springer-Verlag, 1998.Google Scholar - 5.S. Hermann and R. Klette. A comparative study on 2D curvature estimators.
*Proc. of the Int’l. Conf. on Computing: Theory and Applications*, pages 584–589, 2007.Google Scholar - 6.D. G. Kendall. The diffusion of shape.
*Advances in Applied Probability*, 9(3):428–430, 1977.CrossRefGoogle Scholar - 7.D. G. Kendall, D. Barden, T. K. Carne, and H. Le.
*Shape and shape theory*. John Wiley & Sons, 1999.Google Scholar - 8.K. Krish, S. Heinrich, W. E. Snyder, H. Cakir, and S. Khorram. Global registration of overlapping images using accumulative image features.
*Pattern Recognition Letters*, 31:112–118, 2010.CrossRefGoogle Scholar - 9.T. P. Nguyen and I. Debled-Rennesson. Curvature estimation in noisy curves.
*Proc. of the 12th Int’l Conf. on Computer Analysis of Images and Patterns*, pages 474–481, 2007.Google Scholar - 10.J. Opera.
*Differential geometry and its applications*, chapter 1, page 17. Pearson Education, 2007.Google Scholar - 11.W. E. Snyder. A strategy for shape recognition. In A. Srivastava, editor,
*Workshop on Challenges and Opportunities in Image Understanding*, College Park, MD, Jan. 2007.Google Scholar - 12.F. Zhou and F. De la Torre. Canonical time warping for alignment of human behavior.
*Advances in Neural Information Processing Systems 22*, pages 2286–2294, 2009.Google Scholar - 13.F. Zhou and F. De la Torre. Generalized time warping for multi-modal alignment of human motion.
*IEEE Conf. on Computer Vision and Pattern Recognition*, pages 1282–1289, June 2012.Google Scholar