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Research Interests: Information Systems, Algorithms, Artificial Intelligence, Image Processing, Machine Learning, and 14 moreImage Analysis, Face, Computer Security, Biometry, Facial expression, Humans, Motion, Texture Analysis, Image Enhancement, Local Binary Pattern, Reproducibility of Results, Sensitivity and Specificity, Electrical And Electronic Engineering, and Video Recording
... The method uses aligned silhouettes and silhouettes are readily available in their database. ... In the first scenario, we show the proposed LBP-based features cluster even without a powerful modeling method. ... 3.1 Feature Analysis... more
... The method uses aligned silhouettes and silhouettes are readily available in their database. ... In the first scenario, we show the proposed LBP-based features cluster even without a powerful modeling method. ... 3.1 Feature Analysis ...
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We present a novel approach for human activity reco gnition. The method uses dynamic texture descriptors to describe human movements in a spatiotemporal way. The same features are also use d for human detection, which makes our whole... more
We present a novel approach for human activity reco gnition. The method uses dynamic texture descriptors to describe human movements in a spatiotemporal way. The same features are also use d for human detection, which makes our whole approach computationally simple. Following recent trends in computer vision research , our method works on image data rather than silhouettes. We test
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Gait is a biometric feature and identification of people from gait captured on video that has become a challenging problem in computer vision. A recognition method based on amplitude spectrum (Fourier spectrum) of frequency domain is... more
Gait is a biometric feature and identification of people from gait captured on video that has become a challenging problem in computer vision. A recognition method based on amplitude spectrum (Fourier spectrum) of frequency domain is proposed. Fourier spectrum reflects the frequency feature of person's pose in the current frame. After getting the period and key poses through the whole
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We present a discriminative approach to human action recognition. At the heart of our approach is the use of common spatial patterns (CSP), a spatial filter technique that transforms temporal feature data by using differences in variance... more
We present a discriminative approach to human action recognition. At the heart of our approach is the use of common spatial patterns (CSP), a spatial filter technique that transforms temporal feature data by using differences in variance between two classes. Such a transformation focuses on differences between classes, rather than on modeling each class individually. As a result, to distinguish
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Dynamic textures are image sequences with visual pattern repetition in time and space, such as smoke, flames, moving objects and so on. Dynamic texture synthesis is to provide a continuous and infinitely varying stream of images by doing... more
Dynamic textures are image sequences with visual pattern repetition in time and space, such as smoke, flames, moving objects and so on. Dynamic texture synthesis is to provide a continuous and infinitely varying stream of images by doing operations on dynamic textures. Considering that the previous video texture method provides high-quality visual results, but its representation does not well explore the temporal correlation among frames, we develop a novel spatial temporal descriptor for frame description accompanied with a similarity measure on the basis of the video texture method. Compared with the previous one, our method considers both the spatial and temporal domains of video sequences in representation; moreover, combines the local and global description on each spatial-temporal plane. From experimental results, the proposed method achieves better performance in both the syntheses of natural scene and human motion. Especially, it has the characteristic to be robust to noise in remodeling videos into infinite time domain.
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Facial expression recognition is to determine the emotional state of the face regardless of its identity. Most of the existing datasets for facial expressions are captured in a visible light spectrum. However, the visible light (VIS) can... more
Facial expression recognition is to determine the emotional state of the face regardless of its identity. Most of the existing datasets for facial expressions are captured in a visible light spectrum. However, the visible light (VIS) can change with time and location, causing significant variations in appearance and texture. In this paper, we present a novel research on a dynamic
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Facial expressions can be thought as specific dynamic textures where local appearance and motion information need to be taken into account. We utilize local spatiotemporal operators to describe facial expressions. All current facial... more
Facial expressions can be thought as specific dynamic textures where local appearance and motion information need to be taken into account. We utilize local spatiotemporal operators to describe facial expressions. All current facial expression recognition databases are captured in visible light spectrum. Visual light usually changes with locations, and can also vary with time, which can cause significant variations in image appearance and texture. In this paper, we present a novel research on a dynamic facial expression recognition from near-infrared (NIR) video sequences. NIR imaging is robust with respect to illumination changes. Experiments on a new NIR database show promising and robust results against illumination variations.
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Due to its excellent performance, robustness to illumination variations and computational efficiency the LBP has been used in a wide variety of different image analysis problems and applications around the world. Among the most important... more
Due to its excellent performance, robustness to illumination variations and computational efficiency the LBP has been used in a wide variety of different image analysis problems and applications around the world. Among the most important areas of application are face analysis, biometrics, biomedical image analysis, industrial inspection and video analysis. This chapter presents a brief introduction to some representative papers from different application areas.
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Methods for analyzing humans and their actions from monocular or multi-view video data are required in many different applications. In this chapter simple LBP-based approaches for action recognition are introduced. The methods perform... more
Methods for analyzing humans and their actions from monocular or multi-view video data are required in many different applications. In this chapter simple LBP-based approaches for action recognition are introduced. The methods perform very favorably compared to the state-of-the-art for test video sequences commonly used in the research community.
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In this chapter, the LBP operator is extended to spatiotemporal domain. Two different versions, Volume LBP (VLBP) and Local Binary Patterns from Three Orthogonal Planes (LBP-TOP) are introduced. An effective rotation-invariant version of... more
In this chapter, the LBP operator is extended to spatiotemporal domain. Two different versions, Volume LBP (VLBP) and Local Binary Patterns from Three Orthogonal Planes (LBP-TOP) are introduced. An effective rotation-invariant version of the simple LBP-TOP operator is also presented. Finally, some recent variants of the spatiotemporal LBP are briefly described.
Automatic categorization of human actions in the real world is very challenging due to the great intra-class differences. In this paper, we present a new method for robust recognition of human actions. We first cluster each video in the... more
Automatic categorization of human actions in the real world is very challenging due to the great intra-class differences. In this paper, we present a new method for robust recognition of human actions. We first cluster each video in the training set into temporal semantic segments by a dense descriptor. Each segment in the training set is represented by a concatenated
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We present a novel approach for human activity reco gnition. The method uses dynamic texture descriptors to describe human movements in a spatiotemporal way. The same features are also use d for human detection, which makes our whole... more
We present a novel approach for human activity reco gnition. The method uses dynamic texture descriptors to describe human movements in a spatiotemporal way. The same features are also use d for human detection, which makes our whole approach computationally simple. Following recent trends in computer vision research , our method works on image data rather than silhouettes. We test
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Dynamic texture is an extension of texture to the temporal domain. In this paper, a new method for recognizing dynamic textures is proposed. The textures are modeled with concatenated local binary patterns in three orthonormal planes. The... more
Dynamic texture is an extension of texture to the temporal domain. In this paper, a new method for recognizing dynamic textures is proposed. The textures are modeled with concatenated local binary patterns in three orthonormal planes. The circular neighborhoods are generalized to elliptical sampling to fit to the space-time statistics. This is an extension of the LBP approach widely used
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... Our eye detector is inspired by the works of Viola and Jones on the use of Haar-like features with integral images [27 ... The movements of mouth regions are described using local binary patterns from XY, XT and YT planes, combining... more
... Our eye detector is inspired by the works of Viola and Jones on the use of Haar-like features with integral images [27 ... The movements of mouth regions are described using local binary patterns from XY, XT and YT planes, combining local features from pixel, block and volume ...
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... Cell Carcioma SIFT Scale Invariant Feature Transform SILTP Scale Invariant Local Ternary Pattern SIMD ... A popular way M. Pietikäinen et al., Computer Vision Using Local Binary Patterns ... this progress the application areas of... more
... Cell Carcioma SIFT Scale Invariant Feature Transform SILTP Scale Invariant Local Ternary Pattern SIMD ... A popular way M. Pietikäinen et al., Computer Vision Using Local Binary Patterns ... this progress the application areas of texture analysis will also be covering such modern ...
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Abstract In this paper, we propose an integrated framework for tracking, modelling and recognition of facial expressions. The main contributions are:(i) a view-and texture independent scheme that exploits facial action parameters... more
Abstract In this paper, we propose an integrated framework for tracking, modelling and recognition of facial expressions. The main contributions are:(i) a view-and texture independent scheme that exploits facial action parameters estimated by an appearance-based 3D face tracker;(ii) the complexity of the non-linear facial expression space is modelled through a manifold, whose structure is learned using Laplacian Eigenmaps. The projected facial expressions are afterwards recognized based on Nearest Neighbor ...
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ABSTRACT In this paper, a feature extraction method is developed for texture description. To obtain discriminative patterns, we present a learning framework which is formulated into a three-layered model. It can estimate the optimal... more
ABSTRACT In this paper, a feature extraction method is developed for texture description. To obtain discriminative patterns, we present a learning framework which is formulated into a three-layered model. It can estimate the optimal pattern subset of interest by simultaneously considering the robustness, discriminative power and representation capability of features. This model is generalized and can be integrated with existing LBP variants such as conventional LBP, rotation invariant patterns, local patterns with anisotropic structure, completed local binary pattern (CLBP) and local ternary pattern (LTP) to derive new image features for texture classification. The derived descriptors are extensively compared with other widely used approaches and evaluated on two publicly available texture databases (Outex and CUReT) for texture classification, two medical image databases (Hela and Pap-smear) for protein cellular classification and disease classification, and a neonatal facial expression database (infant COPE database) for facial expression classification. Experimental results demonstrate that the obtained descriptors lead to state-of-the-art classification performance.