The Community for Technology Leaders
RSS Icon
Issue No.07 - July (2010 vol.32)
pp: 1335-1341
Ahmed Bilal Ashraf , Carnegie Mellon University, Pittsburgh
Simon Lucey , Commonwealth Science and Industrial Research Organization (CSIRO), Australia
Tsuhan Chen , Carnegie Mellon University, Pittsburgh and Cornell University, Ithaca
Linear filters are ubiquitously used as a preprocessing step for many classification tasks in computer vision. In particular, applying Gabor filters followed by a classification stage, such as a support vector machine (SVM), is now common practice in computer vision applications like face identity and expression recognition. A fundamental problem occurs, however, with respect to the high dimensionality of the concatenated Gabor filter responses in terms of memory requirements and computational efficiency during training and testing. In this paper, we demonstrate how the preprocessing step of applying a bank of linear filters can be reinterpreted as manipulating the type of margin being maximized within the linear SVM. This new interpretation leads to sizable memory and computational advantages with respect to existing approaches. The reinterpreted formulation turns out to be independent of the number of filters, thereby allowing the examination of the feature spaces derived from arbitrarily large number of linear filters, a hitherto untestable prospect. Further, this new interpretation of filter banks gives new insights, other than the often cited biological motivations, into why the preprocessing of images with filter banks, like Gabor filters, improves classification performance.
Gabor filters, support vector machine, maximum margin, expression recognition.
Ahmed Bilal Ashraf, Simon Lucey, Tsuhan Chen, "Reinterpreting the Application of Gabor Filters as a Manipulation of the Margin in Linear Support Vector Machines", IEEE Transactions on Pattern Analysis & Machine Intelligence, vol.32, no. 7, pp. 1335-1341, July 2010, doi:10.1109/TPAMI.2010.75
[1] D. Gabor, "Theory of Communication," J. IEE (London), vol. 93, no. III, pp. 429-457, 1946.
[2] J.G. Daugman, "Two-Dimensional Spectral Analysis of Cortical Receptive Field Profiles," Vision Research, vol. 20, no. 10, pp. 847-856, 1980.
[3] J. Daugman, "Uncertainty Relation for Resolution in Space, Spatial Frequency, and Orientation Optimized by Two-Dimensional Cortical Filters," J. Optical Soc. of Am., vol. 2, no. 7, pp. 1160-1169, 1985.
[4] J.G. Daugman, "Complete Discrete 2-D Gabor Transforms by Neural Networks for Image Analysis and Compression," IEEE Trans. Acoustics, Speech, and Signal Processing, vol. 36, no. 7, pp. 1169-1179, July 1988.
[5] D.J. Field, "Relations Between the Statistics of Natural Images and the Response Properties of Cortical Cells," J. Optical Soc. of Am. A, vol. 4, no. 12, pp. 2379-2393, 1987.
[6] C. Wiskott, J.M. Fellous, N. Krüger, and C. von der Malsburg, "Face Recognition by Elastic Bunch Graph Matching," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 19, no. 7, pp. 775-779, July 1997.
[7] C. Liu and H. Wechsler, "Gabor Feature Based Classification Using the Enhanced Fisher Linear Discriminant Model for Face Recognition," IEEE Trans. Image Processing, vol. 11, no. 4, pp. 467-476, Apr. 2002.
[8] M.S. Bartlett, G. Littlewort, M. Frank, C. Lainscesk, I. Fasel, and J. Movellan, "Recognizing Facial Expression: Machine Learning and Application to Spontaneous Behavior," Proc. IEEE Conf. Computer Vision and Pattern Recognition, vol. 2, pp. 568-573, June 2005.
[9] M. Bartlett, G. Littlewort, C. Lainscsek, I. Fasel, M. Frank, and J. Movellan, "Fully Automatic Facial Action Recognition in Spontaneous Behavior," Proc. Seventh IEEE Int'l Conf. Automatic Face and Gesture Recognition, 2006.
[10] Z. Li, D. Lin, and X. Tang, "Nonparametric Discriminant Analysis for Face Recognition," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 31, no. 4, pp. 755-761, Apr. 2009.
[11] P.K. Shivaswamy and T. Jebara, "Relative Margin Machines," Proc. Neural Information Processing Systems, vol. 21, 2008.
[12] A.V. Oppenheim and A.S. Willsky, Signals & Systems, second ed. Prentice Hall, 1996.
[13] T. Kanade, J.F. Cohn, and Y. Tian, "Comprehensive Database for Facial Expression Analysis," Proc. IEEE Int'l Conf. Automatic Face and Gesture Recognition, pp. 46-53, 2000.
[14] B.E. Boser, I. Guyon, and V. Vapnik, "A Training Algorithm for Optimal Margin Classifiers," Proc. Fifth Ann. ACM Workshop Computational Learning Theory, 1992.
[15] R.-E. Fan, K.-W. Chang, C.-J. Hsieh, X.-R. Wang, and C.-J. Lin, "LIBLINEAR: A Library for Large Linear Classification," J. Machine Learning Research, vol. 9, pp. 1871-1874, 2008.
[16] C.-C. Chang and C.-J. Lin, LIBSVM: A Library for Support Vector Machines,, 2001.
[17] P. Ekman and W.V. Friesen, Facial Action Coding System. Consulting Psychologists Press, 1978.
[18] P. Ekman, W.V. Friesen, and J. Hager, Facial Action Coding System: Research Nexus. Network Research Information, 2002.
[19] S. Lucey, A.B. Ashraf, and J. Cohn, "Investigating Spontaneous Facial Action Recognition Through AAM Representations of the Face," Face Recognition Book, K. Kurihara, ed., Pro Literatur Verlag, Apr. 2007.
[20] S. Lucey and T. Chen, "Learning Patch Dependencies for Improved Pose Mismatched Face Verification," Proc. IEEE Int'l Conf. Computer Vision and Pattern Recognition, June 2006.
[21] J. Cohn, A. Zlochower, J.-J.J. Lien, and T. Kanade, "Automated Face Analysis by Feature Point Tracking has High Concurrent Validity with Manual FACS Coding," Psychophysiology, vol. 36, pp. 35-43, 1999.
22 ms
(Ver 2.0)

Marketing Automation Platform Marketing Automation Tool