Convolutional neural network on three orthogonal planes for dynamic texture classification

In this paper, a new dynamic facial expression recognition method is proposed. Dynamic facial expression recognition is formulated as a longitudinal groupwise registration problem. The main contributions of this method lie in the following aspects: 1) subject-specific facial feature movements of different expressions are described by a diffeomorphic growth model; 2) salient longitudinal facial expression atlas is built for each expression by a sparse groupwise image registration method, which can describe the overall facial feature changes among the whole population and can suppress the bias due to large intersubject facial variations; and 3) both the image appearance information in spatial domain and topological evolution information in temporal domain are used to guide recognition by a sparse representation method. The proposed framework has been extensively evaluated on five databases for different applications: the extended Cohn-Kanade, MMI, FERA, and AFEW databases for dynamic facial expression recognition, and UNBC-McMaster database for spontaneous pain expression monitoring. This framework is also compared with several state-of-the-art dynamic facial expression recognition methods. The experimental results demonstrate that the recognition rates of the new method are consistently higher than other methods under comparison.

Dynamic Facial Expression Recognition With Atlas Construction and Sparse Representation

A novel scheme based on local binary pattern for dynamic texture recognition

Dynamic texture recognition using multiresolution edge-weighted local structure pattern

Dynamic texture is the moving sequence of images that shows some form of temporal regularity. Various static texture descriptors have been extended to spatiotemporal domain for dynamic texture classification. Local Binary Pattern (LBP) is a simple descriptor computationally but sensitive to noise and sometimes fails to capture different patterns. In view of this, a novel approach for dynamic texture classification is introduced that maintains the advantageous characteristics of uniform LBP. Inspired by the Weber's law, a simple yet very powerful, robust texture descriptor, i.e., Weber's law based LBP with center pixel (WLBPC) is proposed from the local patches based on the conventional Local Binary Pattern approach. A noise resistant variant of Weber's law based LBP with center pixel (NR-WLBPC) is also proposed. To do this, WLBPC is extended to a 3-valued code based on a new threshold. Proposed noise resistant variant of WLBPC descriptor makes use of the indecisive bit and the uniform pattern to compute the feature vector. Center pixel information is fused with both the dynamic texture descriptors to improve the discriminative power. Extensive experimental evaluations on representative dynamic texture databases (DynTex++ and UCLA) show that the proposed descriptors show better performance in comparison to recent state-of-the-art LBP variants and other methods under both normal and noisy conditions. Noise invariant of the proposed descriptor also performs better in the presence of noise due to its robustness and discriminating capabilities.

Improved Weber's law based local binary pattern for dynamic texture recognition

Spatiotemporal image descriptors are gaining attention in the image research community for better representation of dynamic textures. In this paper, we introduce a dynamic-micro-texture descriptor, i.e., spatiotemporal directional number transitional graph (DNG), which describes both the spatial structure and motion of each local neighborhood by capturing the direction of natural flow in the temporal domain. We use the structure of the local neighborhood, given by its principal directions, and compute the transition of such directions between frames. Moreover, we present the statistics of the direction transitions in a transitional graph, which acts as a signature for a given spatiotemporal region in the dynamic texture. Furthermore, we create a sequence descriptor by dividing the spatiotemporal volume into several regions, computing a transitional graph for each of them, and represent the sequence as a set of graphs. Our results validate the robustness of the proposed descriptor in different scenarios for expression recognition and dynamic texture analysis.

Short Papers Spatiotemporal Directional Number Transitional Graph for Dynamic Texture Recognition

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Short Papers Spatiotemporal Directional Number Transitional Graph for Dynamic Texture Recognition