Principal component analysis of image gradient orientations for face recognition


Tzimiropoulos, Georgios and Zafeiriou, Stefanos and Pantic, Maja (2011) Principal component analysis of image gradient orientations for face recognition. In: IEEE International Conference on Automatic Face & Gesture Recognition and Workshops, FG 2011, 21-25 March 2011, Santa Barbara, CA (pp. pp. 553-558).

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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 intensitybased PCA. We demonstrate some of its favorable properties for the application of face recognition.
Item Type:Conference or Workshop Item
Copyright:© 2011 IEEE
Electrical Engineering, Mathematics and Computer Science (EEMCS)
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