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

Zafeiriou, Stefanos; Tzimiropoulos, Georgios; Pantic, Maja

Abstract

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.

Citation

Zafeiriou, S., Tzimiropoulos, G., & Pantic, M. (2011). Subspace analysis of arbitrarily many linear filter responses with an application to face tracking.

• Conference Name: 2011 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (CBPRW)
• End Date: Jun 25, 2011
• Publication Date: Jan 1, 2011
• Deposit Date: Feb 1, 2016
• Publicly Available Date: Feb 1, 2016
• Peer Reviewed: Peer Reviewed
• Public URL: https://nottingham-repository.worktribe.com/output/1011509
• Publisher URL: http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=5981738
• Additional Information: © 2011 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. ; Published in: 2011 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) IEEE, 2011, ISBN 9781457705298. doi: 10.1109/CVPRW.2011.5981809