Gabor feature-based face recognition using supervised locality preserving projection

Zheng, Zhonglong; Yang, Fan; Tan, Wenjun; Jia, Jiong; Yang, Jie

doi:10.1016/j.sigpro.2007.03.006

Cited by 105 publications

(26 citation statements)

References 19 publications

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“…This section is allocated to a brief description of the features of Gabor whose full details are presented in [16] which is defined in a convolution image (2): III. OLPP In recent years, many studies have shown that the characteristics of MRI images in a nonlinear subspace are manifold (Saul & Roweis, 2003), However, PCA and LDA can only discover Global Euclidean Structure and are not capable of discovering the underlying structure of MRI images which are located in non-linear manifolds.…”

Section: B Gabor Featuresmentioning

confidence: 99%

Brain Tumor Diagnosis Based on Orthogonal Local Storage Mapping Based on Gabor

Mortaziyan¹,

Motameni²

2017

IJARCSSE

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Abstract-The present work introduces a new method for classifying brain tumor features using the extracted features from filtered brain images. Global feature extraction techniques such as Linear Discriminate Analysis (LDA), characteristics of the overall structure of MRI images and local information preserving techniques such as Locality Preserving Projection (OLPP) Local features would maintain the manifold local features of the same images, to exploit the information that can retain the geometry of images to avoid confusion when changes occur. The proposed method is based on the conventional features of the images with some added features for the detection of tumors. To enhance the diagnosis capacity and the hidden features of brain tumor images, firstly Gabor features were determined through the Gabor filter bank of MRI images. These features proved more effective and improved than the original brain images. Then, OLPP was used to extract the conventional features. This included some new features which were different separating information of Gabor features. New images could be classified in terms of the extracted features using a classifier based on the nearest neighbor. The proposed algorithm was used based on a database with 6 other methods. The results showed that the proposed method would show higher detection rates than other detection methods based on the features.

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Section: B Gabor Featuresmentioning

confidence: 99%

Brain Tumor Diagnosis Based on Orthogonal Local Storage Mapping Based on Gabor

Mortaziyan¹,

Motameni²

2017

IJARCSSE

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“…Gabor filters exhibit desirable characteristics of Spatial frequency(scaling),localization and Orientation selectivity and Gabor filter representation of face images(Gabor faces) are robust for illumination and expressional variability, so are used for face recognition. The dimensionality of Gabor feature space are intensively high, because Gabor faces are obtained by convolution of the face with the dozens of Gabor wavelets(filters).So ,compressed methods are employed to reduce the space dimension to avoid dealing with enormous data [77,78,79]. PCA, 2DPCA, LDA are most popular used dimension reduction algorithms [77,78,79].…”

Section: Overview Of Gabor Waveletmentioning

confidence: 99%

“…The dimensionality of Gabor feature space are intensively high, because Gabor faces are obtained by convolution of the face with the dozens of Gabor wavelets(filters).So ,compressed methods are employed to reduce the space dimension to avoid dealing with enormous data [77,78,79]. PCA, 2DPCA, LDA are most popular used dimension reduction algorithms [77,78,79]. And, we applied PCA algorithm toper form dimension reduction for feature extraction.…”

Section: Overview Of Gabor Waveletmentioning

confidence: 99%

Face Recognition and Detection through Similarity Measurements

Bashir¹,

Koul²

2017

IJCA

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The facial recognition has been a problem worked on around the world for many persons, the problem has emerged in multiple fields and sciences, especially in computer science and other fields that are very interested in this technology are robotic, criminalist etc. Unfortunately, many reported face recognition techniques relay on the size and representative of training set such as e-passport, law enforcement and id-card identification, and most of them will suffer serious performance drop if only one training sample per person is available to the systems [1].In a face image, only a part of face is changed due to pose, illumination and other source of changes .In this paper, a novel face recognition detection approach known as Gabor wavelet based PCA approach is presented based on fusing global and local features of image. To extract global and local features, Gabor wavelet filter are applied on the whole image and non-overlapping sub-images with equal size. To reduce the dimension of new fused feature vector and to better characterize the similarity between each gallery face and the probe image set, Principal Component Analysis (PCA) is employed. And finally, measure the similarity between the images by using the Euclidean distance as classifier. The Experimental results shows that proposed technique improves the efficiency of face recognition under varying illumination, expression and variation in poses of face images by using standard databases when compared to traditional PCA and Conventional method such as global Gabor faces recognition. In this paper, the proposed algorithm is tested on the public and largely used ORL database.

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“…As the baseline method, we use the 1-nearest neighbor (1-NN) classifier in the original feature space. Similarly, we apply 1-NN classifier to the subspace obtained by LDA [25], supervised LPP [26], D-GPLVM [20] and GPLRF [27]. We also compared VC-GPM to several stateof-the-art methods for multi-view learning, namely, the Multi-view Discriminant Analysis (mvDA) [28], and methods for Generalized Multiview Analysis (GMA) [29], namely, GM Linear Discriminant Analysis (GMLDA) and GM Locality Preserving Projections (GMLPP), which extend the LDA and LPP [30] to multiple views.…”

Section: Datasets and Experimental Proceduresmentioning

confidence: 99%

View-Constrained Latent Variable Model for Multi-view Facial Expression Classification

Eleftheriadis

Rudovic

Pantić

2014

Advances in Visual Computing

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Abstract. We propose a view-constrained latent variable model for multi-view facial expression classification. In this model, we first learn a discriminative manifold shared by multiple views of facial expressions, followed by the expression classification in the shared manifold. For learning, we use the expression data from multiple views, however, the inference is performed using the data from a single view. Our experiments on data of posed and spontaneously displayed facial expressions show that the proposed approach outperforms the state-of-the-art methods for multi-view facial expression classification, and several state-of-theart methods for multi-view learning.

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Gabor feature-based face recognition using supervised locality preserving projection

Cited by 105 publications

References 19 publications

Brain Tumor Diagnosis Based on Orthogonal Local Storage Mapping Based on Gabor

Brain Tumor Diagnosis Based on Orthogonal Local Storage Mapping Based on Gabor

Face Recognition and Detection through Similarity Measurements

View-Constrained Latent Variable Model for Multi-view Facial Expression Classification

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