Orthogonal Laplacianfaces for Face Recognition

Cai, Deng; He, Xiaofei; Han, Jiawei; Zhang, Hong-Jiang

doi:10.1109/tip.2006.881945

Cited by 737 publications

(396 citation statements)

References 17 publications

(25 reference statements)

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“…S UBSPACE learning for computer vision applications has recently attracted a lot of interest in the scientific community [1], [2], [3], [4], [5], [6], [7], [8], [9], [10], [11], [12], [13], [14], [15]. This research has been primarily motivated by the development of a multitude of techniques for the efficient analysis of high-dimensional data via non-linear dimensionality reduction [3], [4], [5], [6], [10], [13].…”

Section: Introductionmentioning

confidence: 99%

Subspace Learning from Image Gradient Orientations

Tzimiropoulos

Zafeiriou

Pantić

2012

IEEE Trans. Pattern Anal. Mach. Intell.

148

150

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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 http://ibug.doc.ic.ac.uk/resources.

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Section: Introductionmentioning

confidence: 99%

Subspace Learning from Image Gradient Orientations

Tzimiropoulos

Zafeiriou

Pantić

2012

IEEE Trans. Pattern Anal. Mach. Intell.

148

150

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“…However, it does not work well if the scale of buildings in visiual scenes changes greatly. In this paper, we switch to Orthogonal Locality Preserving Projections (OLPP) [14], since it is more stable and has more locality preserving power than LPP.…”

Section: B Manifold Learning and Feature Extraction Processmentioning

confidence: 99%

Dynamic Building Tracking from UAVs Based on Image Manifold Learning

Zhang¹,

Ren²

2012

Proceedings of the 2012 2nd International Conference on Computer and Information Applications (ICCIA 2012)

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Abstract-Fast and accurate visual tracking of ground buildings can provide unmanned aerial vehicles (UAVs) with rich perceptual information, which is very important for target recognition, navigation and system control. However, when an UAV moves fast, both background and buildings in visual scenes change relatively and rapidly. Consequently, there are no constant features for objects' appearance, which poses great challenges for visual tracking of buildings. In this paper, we first build an image manifold of buildings, which can encode the continuous variation of appearance. We then propose an efficient approach to learn this manifold and obtain more robust feature extraction results. By using a simple tracking framework, we successfully apply the extracted lowdimensional features to real-time building tracking. Experimental results demonstrate the effectiveness of the proposed method.

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“…Such representative methods include e.g. ISOMAP [2], Locally Linear Embedding (LLE) [3], Locality Preserving Projections [4], Orthogonal Locality Preserving Projections (OLPP) [5] and Neighborhood Preserving Embedding (NPE) [6].…”

Section: Introductionmentioning

confidence: 99%

Maximum Margin Projection Subspace Learning for Visual Data Analysis

Nikitidis

Tefas

Pitas

2014

IEEE Trans. on Image Process.

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Abstract-Visual pattern recognition from images often involves dimensionality reduction as a key step to discover a lower dimensional image data representation and obtain a more manageable problem. Contrary to what is commonly practiced today in various recognition applications where dimensionality reduction and classification are independently treated, we propose a novel dimensionality reduction method appropriately combined with a classification algorithm. The proposed method called maximum margin projection pursuit, aims to identify a low dimensional projection subspace, where samples form classes that are better discriminated, i.e., are separated with maximum margin. The proposed method is an iterative alternate optimization algorithm that computes the maximum margin projections exploiting the separating hyperplanes obtained from training a support vector machine classifier in the identified low dimensional space. Experimental results on both artificial data, as well as, on popular databases for facial expression, face and object recognition verified the superiority of the proposed method against various state-of-the-art dimensionality reduction algorithms.

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Orthogonal Laplacianfaces for Face Recognition

Cited by 737 publications

References 17 publications

Subspace Learning from Image Gradient Orientations

Subspace Learning from Image Gradient Orientations

Dynamic Building Tracking from UAVs Based on Image Manifold Learning

Maximum Margin Projection Subspace Learning for Visual Data Analysis

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