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IEEE 14th International Conference on Machine Learning and Applications ICMLA

作者： Camlica, Zehra Tizhoosh, H. R. Khalvati, Farzad Univ Waterloo KIMIA Lab Waterloo ON N2L 3G1 Canada Sunnybrook Hlth Sci Ctr Toronto ON Canada

ISBN: (纸本)9781509002870

Good results on image classification and retrieval using support vector machines (SVM) with local binary patterns (LBPs) as features have been extensively reported in the literature where an entire image is retrieved or classified. In contrast, in medical imaging, not all parts of the image may be equally significant or relevant to the image retrieval application at hand. For instance, in lung x-ray image, the lung region may contain a tumour, hence being highly significant whereas the surrounding area does not contain significant information from medical diagnosis perspective. In this paper, we propose to detect salient regions of images during training and fold the data to reduce the effect of irrelevant regions. As a result, smaller image areas will be used for LBP features calculation and consequently classification by SVM. We use IRMA 2009 dataset with 14,410 x-ray images to verify the performance of the proposed approach. The results demonstrate the benefits of saliency-based folding approach that delivers comparable classification accuracies with state-of-the-art but exhibits lower computational cost and storage requirements, factors highly important for big data analytics.

关键词： Image classification saliency folding local binary patterns support vector machines

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Learning the Discriminate Patches from the Key Landmarks for Facial Expression Recognition

Learning the Discriminate Patches from the Key Landmarks for...

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2015 IEEE International Conference on Smart City/SocialCom/SustainCom (SmartCity)

作者： Wang, Xun Liu, Xingang Univ Elect Sci & Technol China Dept Elect Engn Chengdu Peoples R China

ISBN: (纸本)9781509018932

Extraction of discriminate features which could represent the facial expression accurately plays a vital role in effective Facial Expression Recognition (FER). Although much progress has been made, selecting the discriminate features is still a challenging and interesting problem in the FER system. In this paper, we propose a new FER method, which uses the active shape mode (ASM) algorithm to landmark the key points of face, then extracts local binary patterns (LBP) features around the key points and uses the Multi-Task Learning (MTL) algorithm to select the discriminate patches to represent facial expression accurately. Then we use uses support vector machine (SVM) classifier to predict the facial emotion. Experiments on the Extended Cohn-Kanada database show that the proposed method has a promising performance and realizes the recognition rate of 96.44%.

关键词： facial expression recognition multil-task learning select patches local binary patterns support vector machine

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An E-learning System With Multifacial Emotion Recognition Using Supervised Machine Learning 7

An E-learning System With Multifacial Emotion Recognition Us...

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7th IEEE International Conference on Technology for Education (T4E)

作者： Ashwin, T. S. Jose, Jijo Raghu, G. Reddy, G. Ram Mohana Natl Inst Technol Karnataka Dept Informat Technol Mangalore India

ISBN: (纸本)9781467395090

E-Learning systems based on Affective computing are popularly used for emotional/behavioral analysis of the users. Emotions expressed by the user is depicted by detecting the facial expression of the user and accordingly the teaching strategies will be changed. The present eLearning systems mainly focus on the single user face detection. Hence, in this paper, we propose multiuser face detection based eLearning system using support vector machine based supervised machine learning technique. Experimental results demonstrate that the proposed system provides the accuracy of 89% to 100% w.r.t different datasets (LFW, FDDB, and YFD). Further, to improve the speed of emotional feature processing, we used GPU along with the CPU and thereby achieve a speedup factor of 2.

关键词： Affective Computing eLearning Facial expression recognition Machine Learning Active Appearance Model local binary patterns

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ADJUSTABLE ADABOOST CLASSIFIER AND PYRAMID FEATURES FOR IMAGE-BASED CERVICAL CANCER DIAGNOSIS 12

ADJUSTABLE ADABOOST CLASSIFIER AND PYRAMID FEATURES FOR IMAG...

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IEEE 12th International Symposium on Biomedical Imaging

作者： Xu, Tao Kim, Edward Huang, Xiaolei Lehigh Univ Comp Sci & Engn Dept Bethlehem PA 18015 USA Villanova Univ Dept Comp Sci Villanova PA 19085 USA

ISBN: (纸本)9781479923748

Cervical cancer is the third most common type of cancer in women worldwide. Most death cases of cervical cancer occur in less developed areas of the world. In this work, we develop an automated and low-cost method that is applicable in those low-resource regions. First, we propose a more distinctive multi-feature descriptor for encoding the cervical image information by enhancing an existing descriptor with the pyramid histogram of local binary pattern (PLBP) feature. Second, we apply the AdaBoost algorithm to perform feature selection, and train a binary classifier to differentiate high-risk patient visits from low-risk patient visits. Our AdaBoost classifier can be adjusted to achieve high specificity, which is necessary for use in clinical practice. Experiments on both balanced and imbalanced datasets are conducted to evaluate the effectiveness of our method. Our method is shown to achieve better performance than existing image-based CIN classification systems and also outperform human interpretations on various screening tests.

关键词： Cervical Cancer Screening Computer Aided Diagnosis Image Classification AdaBoost Pyramid Histograms local binary patterns

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A Comparative Study of Geometry,Gabor Wavelets Representation and local binary patterns for Facial Expression Recognition

A Comparative Study of Geometry,Gabor Wavelets Representatio...

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2010 4th International Conference on Intelligent Information Technology Application (IITA2010)

作者： Qiang Zhou,Shiqing Zhang School of Physics and Electronic Engineering Taizhou University Taizhou,China

Facial expression recognition is an interesting and challenging problem and impacts important applications in many areas such as intelligent environments and multimodal human computer ***,Gabor wavelets representation and local binary patterns (LBP) are three types of successful facial features for facial expression *** this paper,the recognition performance of geometry,Gabor wavelets representation and LBP,was compared on facial expression recognition *** results on the popular JAFFE facial expression database demonstrate Gabor wavelets representation obtains the best performance,outperforming geometry and LBP.

关键词： facial expression recognition Geometry Garbor wavelets representation local binary patterns

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FACIAL EXPRESSION RECOGNITION USING CURVELET BASED local binary patterns

FACIAL EXPRESSION RECOGNITION USING CURVELET BASED LOCAL BIN...

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IEEE International Conference on Acoustics, Speech, and Signal Processing

作者： Ashirbani Saha Q.M. Jonathan Wu Department of Electrical and Computer Engineering University of Windsor Windsor ON Canada

ISBN: (纸本)9781424442959

This paper proposes the use of the combination of digital curvelet transform and local binary patterns for recognizing facial expressions from still images. The curvelet transform is applied to the image of a face at a specific scale and orientation. local binary patterns are extracted from the selected curvelet sub-bands to form the descriptive feature set of the expressions. The average of the features of a particular class of expression is considered as the representative feature set of that class. The expression recognition is performed using a nearest neighbor classifier with Chi-square as the dissimilarity metric. Experiments show that our method yields recognition rates of 93percent and 90percent in JAFFE and Cohn-Kanade databases respectively.

关键词： Facial Expression Recognition Curvelets local binary patterns binary binary pattern Facial Recognition expression recognition Nearest neighbor Facial Expression Wavelet Analysis Transform Still images

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Feature extraction for Facial Age Estimation: A Survey

Feature extraction for Facial Age Estimation: A Survey

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International Conference on Wireless Communications, Signal Processing and Networking

作者： Twisha Dhimar Kinjal Mistree Department of Computer Engineering CGPIT Uka Tarsadia University

ISBN: (纸本)9781467393393

Human age estimation is very interesting challenge today also. The prediction of age based on different techniques and by human, itself is very similar now. The major challenges for machine based age estimation are still alive. Challenges are uncontrolled environment, different light settings, variant posing and expression, ageing effects and life style. Human age estimation started with craniofacial growth has now reached to the condition where challenges are changing into opportunities for developing of applications. Applications include Biometrics, forensic investigation during crime, Relationship management over e-commerce, security control, Monitoring and surveillance, entertainment, cosmetology. Computer-based age estimation via facial images includes stages such as pre-processing, feature extraction, feature reduction and lastly classification or regression. Pre-processing provides invariability against different light settings and environment. Feature extraction deals with extraction of features like facial landmarks, shape and texture for further analysis. Feature reduction is considered for faster analysis of feature vectors. Lastly, regression, classification or combination is used for estimation of the age.

关键词： Age estimation Feature extraction Active appearance model Aging pattern subspace Age manifold local binary patterns Gabor filters Bio-inspired features Convolutional neural network Principal Component Analysis

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Digital Image Forgery Detection Based on Shadow Texture Features

Digital Image Forgery Detection Based on Shadow Texture Feat...

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Telecommunications Forum

作者： Ira Tuba Eva Tuba Marko Beko Graduate School of Computer Science John Naisbitt University Bulevar umetnosti 29 11070 Belgrade Serbia Faculty of Mathematics University of Belgrade Studentski trg 16 11000 Belgrade Serbia Universidade Lusófona de Humanidades e Tecnologias Campo Grande 376 1749 - 024 Lisbon Portugal and CTS/UNINOVA Campus da FCT/UNL Monte de Caparica 2829-516 Caparica Portugal

ISBN: (纸本)9781509040872

Digital images are widely used nowadays and there are powerful and convenient software tools for various types of image processing and enhancement. Unfortunately, these tools also facilitate for easy digital image forgery. In this paper we present an algorithm for digital image forgery detection that deals with the situation when some object, together with its shadow, is copied and pasted to some other location in the same or different image. Algorithm is based on the property that shadows do not change the texture of the underlaying surface. Our algorithms uses local binary patterns from shadow and adjacent non-shadow regions and features extracted from their histograms where energy and entropy proved to be the most discriminative. The proposed method was tested on some benchmark forged images and compared with other approaches from literature where it proved to be successful in detection of this type of forgery.

关键词： Digital image forgery detection Shadow consistency Texture consistency local binary patterns

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Micro-Expression Recognition Using Color Spaces

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IEEE TRANSACTIONS ON IMAGE PROCESSING 2015年第12期24卷 6034-6047页

作者： Wang, Su-Jing Yan, Wen-Jing Li, Xiaobai Zhao, Guoying Zhou, Chun-Guang Fu, Xiaolan Yang, Minghao Tao, Jianhua Chinese Acad Sci Inst Psychol Key Lab Behav Sci Beijing 100101 Peoples R China Jilin Univ Key Lab Symbol Computat & Knowledge Engn Minist Educ Changchun 130012 Peoples R China Wenzhou Univ Coll Teacher Educ Wenzhou 325035 Peoples R China Univ Oulu Dept Comp Sci & Engn FI-90014 Oulu Finland Jilin Univ Coll Comp Sci & Technol Changchun 130012 Peoples R China Chinese Acad Sci Inst Psychol State Key Lab Brain & Cognit Sci Beijing 100101 Peoples R China Chinese Acad Sci Inst Automat Natl Lab Pattern Recognit Beijing 100190 Peoples R China

Micro-expressions are brief involuntary facial expressions that reveal genuine emotions and, thus, help detect lies. Because of their many promising applications, they have attracted the attention of researchers from various fields. Recent research reveals that two perceptual color spaces (CIELab and CIELuv) provide useful information for expression recognition. This paper is an extended version of our International Conference on Pattern Recognition paper, in which we propose a novel color space model, tensor independent color space (TICS), to help recognize micro-expressions. In this paper, we further show that CIELab and CIELuv are also helpful in recognizing micro-expressions, and we indicate why these three color spaces achieve better performance. A micro-expression color video clip is treated as a fourth-order tensor, i.e., a four-dimension array. The first two dimensions are the spatial information, the third is the temporal information, and the fourth is the color information. We transform the fourth dimension from RGB into TICS, in which the color components are as independent as possible. The combination of dynamic texture and independent color components achieves a higher accuracy than does that of RGB. In addition, we define a set of regions of interests (ROIs) based on the facial action coding system and calculated the dynamic texture histograms for each ROI. Experiments are conducted on two micro-expression databases, CASME and CASME 2, and the results show that the performances for TICS, CIELab, and CIELuv are better than those for RGB or gray.

关键词： Micro-expression recognition color spaces tensor analysis local binary patterns facial action coding system

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Suitable models for face geometry normalization in facial expression recognition

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JOURNAL OF ELECTRONIC IMAGING 2015年第1期24卷 013005-013005页

作者： Sadeghi, Hamid Raie, Abolghasem A. Amirkabir Univ Technol Dept Elect Engn Tehran 158754413 Iran

Recently, facial expression recognition has attracted much attention in machine vision research because of its various applications. Accordingly, many facial expression recognition systems have been proposed. However, the majority of existing systems suffer from a critical problem: geometric variability. It directly affects the performance of geometric feature-based facial expression recognition approaches. Furthermore, it is a crucial challenge in appearance feature-based techniques. This variability appears in both neutral faces and facial expressions. Appropriate face geometry normalization can improve the accuracy of each facial expression recognition system. Therefore, this paper proposes different geometric models or shapes for normalization. Face geometry normalization removes geometric variability of facial images and consequently, appearance feature extraction methods can be accurately utilized to represent facial images. Thus, some expression-based geometric models are proposed for facial image normalization. Next, local binary patterns and local phase quantization are used for appearance feature extraction. A combination of an effective geometric normalization with accurate appearance representations results in more than a 4% accuracy improvement compared to several state-of-the-arts in facial expression recognition. Moreover, utilizing the model of facial expressions which have larger mouth and eye region sizes gives higher accuracy due to the importance of these regions in facial expression. (C) 2015 SPIE and IS&T

关键词： facial expression recognition face geometry normalization piecewise affine warp local binary patterns local phase quantization support vector machine

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