Subspace learning from image gradient orientations

Tzimiropoulos, Georgios, Stefanos, Zafeiriou and Maja, Pantic (2012) Subspace learning from image gradient orientations. IEEE Transactions on Pattern Analysis and Machine Intelligence, 34 (12). pp. 2454-2466. ISSN 0162-8828

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Abstract—We introduce the notion of subspace learning from image gradient orientations for appearance-based object recognition. As
image data is typically noisy and noise is substantially different from Gaussian, traditional subspace learning from pixel intensities fails
very often to estimate reliably the low-dimensional subspace of a given data population. We show that replacing pixel intensities with
gradient orientations and the 2 norm with a cosine-based distance measure offers, to some extend, a remedy to this problem. Within
this framework, which we coin IGO (Image Gradient Orientations) subspace learning, we first formulate and study the properties of
Principal Component Analysis of image gradient orientations (IGO-PCA). We then show its connection to previously proposed robust
PCA techniques both theoretically and experimentally. Finally, we derive a number of other popular subspace learning techniques,
namely Linear Discriminant Analysis (LDA), Locally Linear Embedding (LLE) and Laplacian Eigenmaps (LE). Experimental results
show that our algorithms outperform significantly popular methods such as Gabor features and Local Binary Patterns and achieve
state-of-the-art performance for difficult problems such as illumination- and occlusion-robust face recognition. In addition to this,
the proposed IGO-methods require the eigen-decomposition of simple covariance matrices and are as computationally efficient as
their corresponding 2 norm intensity-based counterparts. Matlab code for the methods presented in this paper can be found at

Keywords:face recognition
Subjects:G Mathematical and Computer Sciences > G740 Computer Vision
Divisions:College of Science > School of Computer Science
ID Code:7446
Deposited On:06 Feb 2013 21:54

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