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5th IEEE Conference on Industrial Electronics and Applications

作者： Wang, Jian-Gang Wang, Hee Lin Ye, Myint Yau, Wei-Yun Inst Infocomm Res Dept Comp Vis & Image Understanding Singapore 138632 Singapore

ISBN: (纸本)9781424450466

In image-based face profiling classification, face alignment, which transforms the face images in order that the 2D coordinates of the facial features are consistent with each other, is considered as an important preprocessing step before the actual classification to maximize classification accuracy. Unfortunately, accurate face alignment is a time-consuming process and a challenging problem especially for low-resolution face images, where eye localization is a tough task. In this paper, we aim to develop a real-time gender recognition system where face alignment is omitted and the detected face bounding box is used directly. Three measures are employed to compensate for the errors caused by the misalignment: firstly, unaligned faces are included in the training set;secondly local binary patterns (LBPs) and Gabor features are extracted to represent the face images and components, respectively and thirdly a probability model is proposed to fuse holistic- and component-based features with a boosting scheme. The experiments using unaligned and manually aligned faces show that a comparable accuracy can be obtained even without alignment.

关键词： Gender recognition face detection holistic component face alignment local binary patterns

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Enhanced local Texture Feature Sets for Face Recognition Under Difficult Lighting Conditions

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IEEE TRANSACTIONS ON IMAGE PROCESSING 2010年第6期19卷 1635-1650页

作者： Tan, Xiaoyang Triggs, Bill Nanjing Univ Aeronaut & Astronaut Dept Comp Sci & Technol Nanjing 210016 Peoples R China CNRS F-38041 Grenoble 9 France Lab Jean Kuntzmann F-38041 Grenoble 9 France

Making recognition more reliable under uncontrolled lighting conditions is one of the most important challenges for practical face recognition systems. We tackle this by combining the strengths of robust illumination normalization, local texture-based face representations, distance transform based matching, kernel-based feature extraction and multiple feature fusion. Specifically, we make three main contributions: 1) we present a simple and efficient preprocessing chain that eliminates most of the effects of changing illumination while still preserving the essential appearance details that are needed for recognition;2) we introduce local ternary patterns (LTP), a generalization of the local binary pattern (LBP) local texture descriptor that is more discriminant and less sensitive to noise in uniform regions, and we show that replacing comparisons based on local spatial histograms with a distance transform based similarity metric further improves the performance of LBP/LTP based face recognition;and 3) we further improve robustness by adding Kernel principal component analysis (PCA) feature extraction and incorporating rich local appearance cues from two complementary sources-Gabor wavelets and LBP-showing that the combination is considerably more accurate than either feature set alone. The resulting method provides state-of-the-art performance on three data sets that are widely used for testing recognition under difficult illumination conditions: Extended Yale-B, CAS-PEAL-R1, and Face Recognition Grand Challenge version 2 experiment 4 (FRGC-204). For example, on the challenging FRGC-204 data set it halves the error rate relative to previously published methods, achieving a face verification rate of 88.1% at 0.1% false accept rate. Further experiments show that our preprocessing method outperforms several existing preprocessors for a range of feature sets, data sets and lighting conditions.

关键词： Face recognition illumination invariance image preprocessing kernel principal components analysis local binary patterns visual features

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Face Features Extraction Based on multi-scale LBP

Face Features Extraction Based on multi-scale LBP

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2010 2nd International Conference on Signal Processing System(2010年信号处理系统国际会议 ICSPS 2010)

作者： Xiaoshan Liu Lianwen Jin Minghui Du School of Electronic and Information Engineering South China University of Technology Guangzhou 51 School of Electronic and Information Engineering South China University of Technology Guangzhou 51

How to extract the strong features of face image is vital important in the face recognition technology. The extracted features should be robust for variation of illumination and expression. a novel feature extraction algorithm based on wavelet decomposition and LBP is proposed, which makes use of the idea of wavelet multiresolution and local characteristic of LBP. And the features extracted by this way contain holistic and local information that can be robust to identify faces. Experiment results show that the proposed method can effectively be used in face recognition with single training sample per person The performance is better than PCA and original LBP. And the importance of different level's lower coefficients is also analyzed.

关键词： local binary patterns feature extraction wavelet decomposition face recognition

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Lipreading With local Spatiotemporal Descriptors

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IEEE TRANSACTIONS ON MULTIMEDIA 2009年第7期11卷 1254-1265页

作者： Zhao, Guoying Barnard, Mark Pietikainen, Matti Univ Oulu Machine Vis Grp Infotech Oulu & Dept Elect & Informat Engn FI-90014 Oulu Finland Univ Surrey Fac Engn & Phys Sci Guildford GU2 7XH Surrey England

Visual speech information plays an important role in lipreading under noisy conditions or for listeners with a hearing impairment. In this paper, we present local spatiotemporal descriptors to represent and recognize spoken isolated phrases based solely on visual input. Spatiotemporal local binary patterns extracted from mouth regions are used for describing isolated phrase sequences. In our experiments with 817 sequences from ten phrases and 20 speakers, promising accuracies of 62% and 70% were obtained in speaker-independent and speaker-dependent recognition, respectively. In comparison with other methods on AVLetters database, the accuracy, 62.8%, of our method clearly outperforms the others. Analysis of the confusion matrix for 26 English letters shows the good clustering characteristics of visemes for the proposed descriptors. The advantages of our approach include local processing and robustness to monotonic gray-scale changes. Moreover, no error prone segmentation of moving lips is needed.

关键词： Lipreading local binary patterns spatiotemporal descriptors visual speech recognition

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Combining appearance and motion for face and gender recognition from videos

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PATTERN RECOGNITION 2009年第11期42卷 2818-2827页

作者： Hadid, Abdenour Pietikainen, Matti Univ Oulu Machine Vis Grp FI-90014 Oulu Finland

While many works consider moving faces only as collections of frames and apply still image-based methods, recent developments indicate that excellent results can be obtained using texture-based spatiotemporal representations for describing and analyzing faces in videos. inspired by the psychophysical findings which state that facial movements can provide valuable information to face analysis, and also by our recent success in using LBP (local binary patterns) for combining appearance and motion for dynamic texture analysis, this paper investigates the combination of facial appearance (the shape of the face) and motion (the way a person is talking and moving his/her facial features) for face analysis in videos. We propose and study an approach for spatiotemporal face and gender recognition from videos using an extended set of volume LBP features and a boosting scheme. We experiment with several publicly available video face databases and consider different benchmark methods for comparison. our extensive experimental analysis clearly assesses the promising performance of the LBP-based spatiotemporal representations for describing and analyzing faces in videos. (C) 2009 Elsevier Ltd. All rights reserved.

关键词： Face recognition Gender recognition Facial dynamics Boosting local binary patterns Spatiotemporal analysis

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A textural approach for mass false positive reduction in mammography

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COMPUTERIZED MEDICAL IMAGING AND GRAPHICS 2009年第6期33卷 415-422页

作者： Llado, X. Oliver, A. Freixenet, J. Marti, R. Marti, J. Univ Girona IIiA IdIBGi Comp Vis & Robot Grp Girona 17071 Spain

During the last decade several algorithms have been proposed for automatic mass detection in mammographic images. However, almost all these methods suffer from a high number of false positives. In this paper we propose a new approach for tackling this false positive reduction problem. The key point of our proposal is the use of local binary patterns (LBP) for representing the textural properties of the masses. We extend the basic LBP histogram descriptor into a spatially enhanced histogram which encodes both the local region appearance and the spatial structure of the masses. Support Vector Machines (SVM) are then used for classifying the true masses from the ones being actually normal parenchyma. Our approach is evaluated using 1792 ROIs extracted from the DDSM database. The experiments show that LBP are effective and efficient descriptors for mammographic masses. Moreover, the comparison with current methods illustrates that our proposal obtains a better performance. (C) 2009 Elsevier Ltd. All rights reserved.

关键词： Breast cancer Image analysis Mammographic automatic mass detection False positive reduction Textural information local binary patterns

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IDENTIFYING CANCER REGIONS IN VITAL-STAINED MAGNIFICATION ENDOSCOPY IMAGES USING ADAPTED COLOR HISTOGRAMS

IDENTIFYING CANCER REGIONS IN VITAL-STAINED MAGNIFICATION EN...

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16th IEEE International Conference on Image Processing

作者： Sousa, A. Dinis-Ribeiro, M. Areia, M. Coimbra, M. Univ Porto Fac Ciencias Inst Telecomunicacoes Oporto Portugal Porto & Inst Portugues Oncol CINTESIS Fac Med Oporto Portugal Inst Portugues Oncol Francisco Gentil Coimbra Portugal

ISBN: (纸本)9781424456536

In-body imaging technologies such as vital-stained magnification endoscopy pose novel image processing challenges to computer-assisted decision systems given their unique visual characteristics such as reduced color spaces and natural textures. In this paper we will show the potential of using adapted color features combined with local binary patterns, a texture descriptor that has exhibited good adaptation to natural images, for classifying gastric regions into three groups: normal, pre-cancer and cancer lesions. Results exhibit 91% accuracy, confirming that specific research for in-body imaging could be the key for future computer assisted decision systems for medicine.

关键词： Medical image processing pattern recognition local binary patterns color features

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Facial Expression Recognition Based on Fusion Features of LBP and Gabor with LDA

Facial Expression Recognition Based on Fusion Features of LB...

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2nd International Congress on Image and Signal Processing

作者： Bai, Gang Jia, Wanhong Jin, Yang Nankai Univ Coll Informat & Technol Tianjin 30071 Peoples R China

ISBN: (纸本)9781424441303

In this paper, the novel facial expression recognition approach using Fusion Features of LBP and Gabor (FFLG) is proposed. At first, local binary patterns (LBPs) and Gabor wavelet features are extracted from facial images, and then Weighed LBP-Gabor Complex Features (WLGCF) are obtained by weighing and combining. Finally, the WLGCF as recognition features are generated with LDA algorithm to represent facial expression effectively. The experiments show our approach feasible.

关键词： facial expression recognition local binary patterns Gabor wavelet

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International Conference on Multimedia Computing and Systems

作者： Augustine, Kavutse Vianney Huang Dongjun Cent S Univ Sch Informat Sci & Engn Dept Comp Applicat Technol Changsha Hunan Peoples R China

ISBN: (纸本)9781424437566

Measurement of image similarity is important for a number of image processing applications. Image similarity assessment is closely related to image quality assessment and is based on the apparent differences between a degraded image and the original, unmodified image. Automated evaluation of image retrieval systems relies on accurate quality measurement of similarity among the input image and the database images. In this paper, we have treated the image under pixel level where we used the mean squared error (MSE) algorithms for measuring similarity between the input image and the training data images, The mean squared error (MSE) simulations have demonstrated its promise through a set of examples by showing its accuracy and low computation cost, though it didn't show good results with precision on similarity of images and for rotated, translated or flipped images hence we proposed the use of minimum circumscribed circle (concentric circles) with local binary pattern and compare the results to get the best image similarity method. Hence comparing different results some improved performance were observed.

关键词： image similarity mean square error minimum circumscribed circle local binary patterns

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IDENTIFYING CANCER REGIONS IN VITAL-STAINED MAGNIFICATION ENDOSCOPY IMAGES USING ADAPTED COLOR HISTOGRAMS

IDENTIFYING CANCER REGIONS IN VITAL-STAINED MAGNIFICATION EN...

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International Conference on Image Processing

作者： A. Sousa M. Dinis-Ribeiro M. Areia M. Coimbra Instituto de Telecomunicacoes Faculdade de Ciencias da Universidade do Porto CINTESIS/Faculdade de Medicina do Porto and Instituto Portugues de Oncologia - Porto Instituto Portugues de Oncologia - Coimbra

ISBN: (纸本)9781424456536;9781424456543

关键词： Medical image processing Pattern recognition local binary patterns Color features color feature Medical image processing Pattern recognition decision system binary pattern magnification Endoscopy histograms Cancer texture descriptor color spaces images Color

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