Tzimiropoulos, Georgios, 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.
Full content URL: http://www.scopus.com/inward/record.url?eid=2-s2.0...
Full text not available from this repository.
| 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 |
Repository Staff Only: item control page