Principal component analysis of image gradient orientations for face recognition

Tzimiropoulos, Georgios and Zafeiriou, S. and Pantic, M. (2011) Principal component analysis of image gradient orientations for face recognition. In: Conference of 2011 IEEE International Conference on Automatic Face and Gesture Recognition and Workshops, FG 2011, 21-25 March 2011, Santa Barbara, CA.

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Item Type:Conference or Workshop contribution (Paper)
Item Status:Live Archive

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 2 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 2 intensity-based PCA. We demonstrate some of its favorable properties for the application of face recognition. © 2011 IEEE.

Additional Information:Conference Code: 85149
Keywords:Computationally efficient, Data population, Distance measure, Eigen decomposition, Gaussians, Gradient orientations, Image data, Image gradients, Intensity-based, Low-dimensional subspace, Pixel intensities, Covariance matrix, Gesture recognition, Population statistics, Principal component analysis, Face recognition
Subjects:G Mathematical and Computer Sciences > G740 Computer Vision
Divisions:College of Science > School of Computer Science
ID Code:8731
Deposited On:08 Apr 2013 15:19

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