Subspace learning from image gradient orientations

Tzimiropoulos, Georgios, Zafeiriou, Stefanos and Pantic, Maja (2012) Subspace learning from image gradient orientations. IEEE Transactions on Pattern Analysis and Machine Intelligence, 34 (12). pp. 2454-2466. ISSN 1939-3539

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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- ecomposition 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

Item Type: Article
Additional Information: © 2012 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.
Keywords: image gradient orientations, robust principal component analysis, discriminant analysis, non-linear dimensionality reduction, face recognition
Schools/Departments: University of Nottingham, UK > Faculty of Science > School of Computer Science
Identification Number:
Depositing User: Tzimiropoulos, Yorgos
Date Deposited: 29 Jan 2016 11:50
Last Modified: 04 May 2020 20:21

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