Skip to main content

Research Repository

Advanced Search

Subspace learning from image gradient orientations

Tzimiropoulos, Georgios; Zafeiriou, Stefanos; Pantic, Maja

Subspace learning from image gradient orientations Thumbnail


Georgios Tzimiropoulos

Stefanos Zafeiriou

Maja Pantic


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

Journal Article Type Article
Acceptance Date Jan 5, 2012
Online Publication Date Jan 19, 2012
Publication Date Dec 1, 2012
Deposit Date Jan 29, 2016
Publicly Available Date Jan 29, 2016
Journal IEEE Transactions on Pattern Analysis and Machine Intelligence
Print ISSN 0162-8828
Electronic ISSN 1939-3539
Publisher Institute of Electrical and Electronics Engineers
Peer Reviewed Peer Reviewed
Volume 34
Issue 12
Pages 2454-2466
Keywords image gradient orientations, robust principal component analysis, discriminant analysis, non-linear dimensionalityreduction, face recognition
Public URL
Publisher URL
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.


Downloadable Citations