Principal component analysis of image gradient orientations for face recognition

Tzimiropoulos, Georgios; Zafeiriou, Stefanos; Pantic, Maja

Abstract

We introduce the notion of Principal Component Analysis (PCA) of image gradient orientations. As image data is typically noisy, but noise is substantially different from Gaussian, traditional PCA of pixel intensities very often fails to estimate reliably the low-dimensional subspace of a given data population. We show that replacing intensities with gradient orientations and the ℓ2 norm with a cosine-based distance measure offers, to some extend, a remedy to this problem. Our scheme requires the eigen-decomposition of a covariance matrix and is as computationally efficient as standard ℓ2 intensity-based PCA. We demonstrate some of its favorable properties for the application of face recognition.

Citation

Tzimiropoulos, G., Zafeiriou, S., & Pantic, M. (2011). Principal component analysis of image gradient orientations for face recognition.

• Conference Name: 2011 IEEE International Conference on Automatic Face & Gesture Recognition (FG 2011)
• End Date: Mar 25, 2011
• Publication Date: Mar 1, 2011
• Deposit Date: Feb 1, 2016
• Publicly Available Date: Feb 1, 2016
• Peer Reviewed: Peer Reviewed
• Keywords: Face recognition, Gradient methods, Principal component analysis
• Public URL: https://nottingham-repository.worktribe.com/output/1010213
• Publisher URL: http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=5771457&punumber%3D5765597%26filter%3DAND%28p_IS_Number%3A5771322%29%26pageNumber%3D4
• 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.