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Robust Facial Expression Recognition via Compressive Sensing

SENSORS 2012年第3期12卷 3747-3761页

作者： Zhang, Shiqing Zhao, Xiaoming Lei, Bicheng Taizhou Univ Dept Comp Sci Taizhou 318000 Peoples R China Taizhou Univ Sch Phys & Elect Engn Taizhou 318000 Peoples R China

Recently, compressive sensing (CS) has attracted increasing attention in the areas of signal processing, computer vision and pattern recognition. In this paper, a new method based on the CS theory is presented for robust facial expression recognition. The CS theory is used to construct a sparse representation classifier (SRC). The effectiveness and robustness of the SRC method is investigated on clean and occluded facial expression images. Three typical facial features, i.e., the raw pixels, Gabor wavelets representation and local binary patterns (LBP), are extracted to evaluate the performance of the SRC method. Compared with the nearest neighbor (NN), linear support vector machines (SVM) and the nearest subspace (NS), experimental results on the popular Cohn-Kanade facial expression database demonstrate that the SRC method obtains better performance and stronger robustness to corruption and occlusion on robust facial expression recognition tasks.

关键词： compressive sensing sparse representation facial expression recognition Gabor wavelets representation local binary patterns corruption and occlusion

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Robustness of Offline Signature Verification Based on Gray Level Features

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IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY 2012年第3期7卷 966-977页

作者： Ferrer, Miguel A. Francisco Vargas, J. Morales, Aythami Ordonez, Aaron Univ Las Palmas Gran Canaria Inst Univ Desarrollo Tecnol & Innovac Comunicac I Las Palmas Gran Canaria 35017 Spain Univ Antioquia Dept Elect Engn MicroE Medellin Colombia

Several papers have recently appeared in the literature which propose pseudo-dynamic features for automatic static handwritten signature verification based on the use of gray level values from signature stroke pixels. Good results have been obtained using rotation invariant uniform local binary patterns LBP8,1riu2 plus LBP16,2riu2 and statistical measures from gray level co-occurrence matrices (GLCM) with MCYT and GPDS offline signature corpuses. In these studies the corpuses contain signatures written on a uniform white "nondistorting" background, however the gray level distribution of signature strokes changes when it is written on a complex background, such as a check or an invoice. The aim of this paper is to measure gray level features robustness when it is distorted by a complex background and also to propose more stable features. A set of different checks and invoices with varying background complexity is blended with the MCYT and GPDS signatures. The blending model is based on multiplication. The signature models are trained with genuine signatures on white background and tested with other genuine and forgeries mixed with different backgrounds. Results show that a basic version of local binary patterns (LBP) or local derivative and directional patterns are more robust than rotation invariant uniform LBP or GLCM features to the gray level distortion when using a support vector machine with histogram oriented kernels as a classifier.

关键词： Application of support vector machine (SVM) biometrics histogram SVM kernels local binary patterns local directional patterns offline signature verification texture features

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ATHEROSCLEROTIC RISK STRATIFICATION STRATEGY FOR CAROTID ARTERIES USING TEXTURE-BASED FEATURES

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ULTRASOUND IN MEDICINE AND BIOLOGY 2012年第6期38卷 899-915页

作者： Acharya, U. Rajendra Sree, S. Vinitha Krishnan, M. Muthu Rama Molinari, Filippo Saba, Luca Ho, Sin Yee Stella Ahuja, Anil T. Ho, Suzanne C. Nicolaides, Andrew Suri, Jasjit S. Global Biomed Technol Inc Roseville CA 95661 USA Ngee Ann Polytech Dept Elect & Comp Engn Singapore Singapore Politecn Torino Biolab Dept Elect Turin Italy Chinese Univ Hong Kong Dept Imaging & Intervent Radiol Hong Kong Hong Kong Peoples R China Chinese Univ Hong Kong Sch Publ Hlth & Primary Care Hong Kong Hong Kong Peoples R China Univ London Imperial Coll Sci Technol & Med Dept Vasc Surg London England Idaho State Univ Aff Dept Biomed Engn Pocatello ID USA Azienda Osped Univ Cagliari Dept Radiol Cagliari Italy

Plaques in the carotid artery result in stenosis, which is one of the main causes for stroke. Patients have to be carefully selected for stenosis treatments as they carry some risk. Since patients with symptomatic plaques have greater risk for strokes, an objective classification technique that classifies the plaques into symptomatic and asymptomatic classes is needed. We present a computer aided diagnostic (CAD) based ultrasound characterization methodology (a class of Atheromatic systems) that classifies the patient into symptomatic and asymptomatic classes using two kinds of datasets: (1) plaque regions in ultrasound carotids segmented semi-automatically and (2) far wall gray-scale intima-media thickness (IMT) regions along the common carotid artery segmented automatically. For both kinds of datasets, the protocol consists of estimating texture-based features in frameworks of local binary patterns (LBP) and Law's texture energy (LTE) and applying these features for obtaining the training parameters, which are then used for classification. Our database consists of 150 asymptomatic and 196 symptomatic plaque regions and 342 IMT wall regions. When using the Atheromatic-based system on semiautomatically determined plaque regions, support vector machine (SVM) classifier was adapted with highest accuracy of 83%. The accuracy registered was 89.5% on the far wall gray-scale IMT regions when using SVM, K-nearest neighbor (KNN) or radial basis probabilistic neural network (RBPNN) classifiers. LBP/LTE-based techniques on both kinds of carotid datasets are noninvasive, fast, objective and cost-effective for plaque characterization and, hence, will add more value to the existing carotid plaque diagnostics protocol. We have also proposed an index for each type of datasets: AtheromaticPi, for carotid plaque region, and AtheromaticWi, for IMT carotid wall region, based on the combination of the respective significant features. These indices show a separation between symptomatic

关键词： Atherosclerosis Symptomatic Asymptomatic Carotid Plaque Far wall local binary patterns Laws' texture energy Wall variability Classifier Support vector machine Risk index

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Face verification of age separated images under the influence of internal and external factors

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IMAGE AND VISION COMPUTING 2012年第12期30卷 1052-1061页

作者： Mahalingam, Gayathri Kambhamettu, Chandra Univ Delaware Dept Comp & Informat Sci Video Image Modeling & Synth Lab Newark DE 19716 USA

In this paper we study the task of face verification of age-separated images with the presence of various internal and external factors. We propose a hierarchical local binary pattern (HLBP) feature descriptor for robust face representation across age. The effective representation by HLBP across minimal age, illumination, and expression variations combined with its hierarchical computation provides a discriminative representation of the face image. The proposed face descriptor is combined with an AdaBoost classification framework to model the face verification task as a two-class problem. Experimental results on the FG-NET and MORPH aging datasets indicate that the performance of the proposed framework is robust with respect to images of both adults and children. A detailed empirical analysis on the effects of internal (age gap, gender, and ethnicity) and external (pose, expressions, facial hair, and glasses) factors in the face verification performance is also studied. The results indicate that the verification accuracy reduces as the age gap between the image pair increases. A quantitative comparison on the effects of gender on verification performance by both humans and the proposed machine learning approach is provided. The analysis indicate that the cues aid humans in verifying image pairs with large age gaps, while it aids machines for all age gaps. However, the cues mislead humans in the case of images of children and extra-personal pairs with large age gaps. Our analyses indicate that the pose and expression variations affect the performance, despite training with such variations, while facial hair and glasses act as discriminative cues. A study on the effects of ethnicity indicate that non-linear algorithms have insignificant effect in performance with the use of both generalized and individual ethnicity models when compared with linear algorithms. (C) 2012 Elsevier B.V. All rights reserved.

关键词： Age progression Face recognition Face verification AdaBoost local binary patterns

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Survey on LBP based texture descriptors for image classification

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EXPERT SYSTEMS WITH APPLICATIONS 2012年第3期39卷 3634-3641页

作者： Nanni, Loris Lumini, Alessandra Brahnam, Sheryl Univ Padua Dept Informat Engn I-35131 Padua Italy Univ Bologna DEIS I-47521 Cesena Italy Missouri State Univ Springfield MO 65804 USA

The aim of this work is to find the best way for describing a given texture using a local binary pattern (LBP) based approach. First several different approaches are compared, then the best fusion approach is tested on different datasets and compared with several approaches proposed in the literature (for fair comparisons, when possible we have used code shared by the original authors). Our experiments show that a fusion approach based on uniform local quinary pattern (LQP) and a rotation invariant local quinary pattern, where a bin selection based on variance is performed and Neighborhood Preserving Embedding (NPE) feature transform is applied, obtains a method that performs well on all tested datasets. As the classifier, we have tested a stand-alone support vector machine (SVM) and a random subspace ensemble of SVM. We compare several texture descriptors and show that our proposed approach coupled with random subspace ensemble outperforms other recent state-of-the-art approaches. This conclusion is based on extensive experiments conducted in several domains using six benchmark databases. (C) 2011 Elsevier Ltd. All rights reserved.

关键词： Texture descriptors local binary patterns local quinary patterns Support vector machines Random subspace

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Delaunay triangulation-based pit density estimation for the classification of polyps in high-magnification chromo-colonoscopy

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COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2012年第3期107卷 565-581页

作者： Haefner, M. Liedlgruber, M. Uhl, A. Vecsei, A. Wrba, F. Salzburg Univ Dept Comp Sci A-5020 Salzburg Austria St Elizabeth Hosp Dept Internal Med Vienna Austria St Anna Childrens Hosp Endoscopy Unit A-1090 Vienna Austria Med Univ Vienna Dept Clin Pathol Vienna Austria

In this work we propose a method to extract shape-based features from endoscopic images for an automated classification of colonic polyps. This method is based on the density of pits as used in the pit pattern classification scheme which is commonly used for the classification of colonic polyps. For the detection of pits we employ a noise-robust variant of the LBP operator. To be able to be robust against local texture variations we extend this operator by an adaptive thresholding. Based on the detected pit candidates we compute a Delaunay triangulation and use the edge lengths of the resulting triangles to construct histograms. These are then used in conjunction with the k-NN classifier to classify images. We show that, compared to a previously developed method, we are not only able to almost always get higher classification results in our application scenario, but that the proposed method is also able to significantly outperform the previously developed method in terms of the computational demand. (C) 2011 Elsevier Ireland Ltd. All rights reserved.

关键词： Colonoscopy Polyps Pit pattern local binary patterns Delaunay triangulation Classification

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A very high performing system to discriminate tissues in mammograms as benign and malignant

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EXPERT SYSTEMS WITH APPLICATIONS 2012年第2期39卷 1968-1971页

作者： Nanni, Loris Brahnam, Sheryl Lumini, Alessandra Dept Informat Engn I-35131 Padua Italy Missouri State Univ Springfield MO 65804 USA Univ Bologna DEIS I-40136 Bologna Italy

In this paper, we compare different state-of-the-art texture descriptors to discriminate tissues in mammograms as either benign or malignant. The three best approaches are the following: (1) A very recent local Ternary Pattern (LTP) variant based on a random subspace of rotation invariant bins with higher variance, where features are transformed using Neighborhood Preserving Embedding (NPE) and then used to train a support vector machine (SVM). The set of SVMs is combined by sum rule. (2) An ensemble of local phase quantization (LPQ) texture descriptors each obtained varying the parameters of LPQ For each descriptor a SVM is trained then the SVMs are combined by sum rule. (3) A method that uses all the uniform bins extracted by LTP for training a random subspace of SVMs. The use of these techniques is very promising when applied to the task of distinguishing benign and malignant breast tissues, with the best approach being to use all the uniform bins extracted by LTP. It obtains an area under the ROC curve (AUC) of 0.97. (C) 2011 Elsevier Ltd. All rights reserved.

关键词： Texture descriptors local binary patterns local Ternary patterns Support vector machines Random subspace

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local binary pattern texture-based classification of solid masses in ultrasound breast images

Local binary pattern texture-based classification of solid m...

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Conference on Medical Imaging - Ultrasonic Imaging, Tomography, and Therapy

作者： Matsumoto, Monica M. S. Sehgal, Chandra M. Udupa, Jayaram K. Univ Penn Perelman Sch Med Dept Radiol Med Image Proc Grp Philadelphia PA 19104 USA Univ Penn Perelman Sch Med Dept Radiol Ultra Sound Res Philadelphia PA 19104 USA

ISBN: (纸本)9780819489692

Breast cancer is one of the leading causes of cancer mortality among women. Ultrasound examination can be used to assess breast masses, complementarily to mammography. Ultrasound images reveal tissue information in its echoic patterns. Therefore, pattern recognition techniques can facilitate classification of lesions and thereby reduce the number of unnecessary biopsies. Our hypothesis was that image texture features on the boundary of a lesion and its vicinity can be used to classify masses. We have used intensity-independent and rotation-invariant texture features, known as local binary patterns (LBP). The classifier selected was K-nearest neighbors. Our breast ultrasound image database consisted of 100 patient images (50 benign and 50 malignant cases). The determination of whether the mass was benign or malignant was done through biopsy and pathology assessment. The training set consisted of sixty images, randomly chosen from the database of 100 patients. The testing set consisted of forty images to be classified. The results with a multi-fold cross validation of 100 iterations produced a robust evaluation. The highest performance was observed for feature LBP with 24 symmetrically distributed neighbors over a circle of radius 3 (LBP24,3) with an accuracy rate of 81.0%. We also investigated an approach with a score of malignancy assigned to the images in the test set. This approach provided an ROC curve with A(z) of 0.803. The analysis of texture features over the boundary of solid masses showed promise for malignancy classification in ultrasound breast images.

关键词： Breast Ultrasound Solid Mass Classification Computer-Aided Diagnosis local binary patterns Texture Analysis

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A Multiple Features Image Tracking Algorithm

A Multiple Features Image Tracking Algorithm

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5th International Symposium on Computational Intelligence and Design (ISCID)

作者： Guo, Wenhua Feng, Zuren Wang, Shuai Nie, Qin Xi An Jiao Tong Univ Syst Engn Inst State Key Lab Mfg Syst Engn Xian 710049 Peoples R China

ISBN: (纸本)9780769548111

In Mean Shift algorithm, the features of the tracked target and the image matching similarity criterion have great influence on the result of tracking. A new algorithm of target tracking is proposed. The algorithm combine local binary pattern and color information to form a new feature CL, which tracks target by using a method of centroid iteration based on maximum posterior probability. Thanks to the simplification of the LBP, the CL has higher differentiation ability and lower computational complexity. Experimental results show that the new algorithm have significantly improved the tracking performance, in comparison with original Mean Shift algorithm. In complex background, the algorithm can track the target robustly.

关键词： object tracking local binary patterns maximum posterior probability multiple features fusion Mean Shift

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Classification of Wood Surface Defects with Hybrid Usage of Statistical and Textural Features

Classification of Wood Surface Defects with Hybrid Usage of ...

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35th International Conference on Telecommunications and Signal Processing (TSP)

作者： Mahram, Amir Shayesteh, Mahrokh G. Jafarpour, Sahar Urmia Univ Dept Elect Engn Orumiyeh Iran

ISBN: (纸本)9781467311182

Machine vision is applied to detect wood knots and cracks, to classify strong and stable woods. In order to obtain effective and efficient classification a well-defined pattern recognition and feature extraction algorithms are essential. In this paper we examine three different methods for feature extraction;Gray level co-occurrence matrix (GLCM), local binary patterns (LBP), and statistical moments. The hybrid usage of these methods is considered. Principal Components Analysis (PCA) and Linear Discriminate Analysis (LDA) are utilized to reduce the feature vector dimension. We use Support Vector Machine (SVM) and K-Nearest Neighbor (KNN) for classification. The classifiers are applied for five different wood knot species. The efficiency of the proposed method using hybrid features called, GLCM+LBF, GLCM+statistical moments and LBF+Statitical moments are investigated through simulations. Comparison with latest works is accomplished to show the capability of the proposed method.

关键词： Wood knots classification Gray level co-occurrence matrix local binary patterns Statistical moments

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