Subspace analysis of arbitrarily many linear filter responses with an application to face tracking

Zafeiriou, S., Tzimiropoulos, Georgios and Pantic, M. (2011) Subspace analysis of arbitrarily many linear filter responses with an application to face tracking. In: Conference of 2011 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2011, 20-25 June 2011, Colorado Springs.

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Multi-scale/orientation local image analysis methods are valuable tools for obtaining highly distinctive image-based representations. Very often, these features are generated from the responses of a bank of linear filters corresponding to different scales and orientations. Naturally, as the number of filters increases, so does the feature dimensionality. Further processing is often feasible only when dimensionality reduction is performed by subspace learning techniques, such as Principal Component analysis (PCA) or Linear Discriminant Analysis (LDA). The major problem stems from the fact that as the number of features increases, so does the computational complexity of these methods which, in turn, limits the number of scales and orientations examined. In this paper, we show how linear subspace analysis on features generated by the response of linear filter banks can be efficiently re-formulated such that complexity does not depend on the number of filters used. We describe computationally efficient and exact versions of PCA while the extension to other subspace learning algorithms is straightforward. Finally, we show how the proposed methods can boost the performance of algorithms for appearance based tracking algorithm. © 2011 IEEE.

Additional Information:Conference Code: 87052
Keywords:Appearance based, Computationally efficient, Different scale, Dimensionality reduction, Face Tracking, Image analysis method, Image-based representation, Linear discriminant analysis, Linear filters, Linear subspace, Multiscales, Performance of algorithm, Subspace analysis, Subspace learning, Tracking algorithm, Computational complexity, Computer vision, Discriminant analysis, Face recognition, Filter banks, Learning algorithms, Principal component analysis
Subjects:G Mathematical and Computer Sciences > G740 Computer Vision
Divisions:College of Science > School of Computer Science
ID Code:8728
Deposited On:07 Apr 2013 20:13

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