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Existing techniques for 3D action recognition are sensitive to viewpoint variations because they extract features from depth images which are viewpoint dependent. In contrast, we directly process pointclouds for cross-view action recognition from unknown and unseen views. We propose the histogram of oriented principal components (HOPC) descriptor that is robust to noise, viewpoint, scale and action...
3D object categorization is a non-trivial task in computer vision encompassing many real-world applications. We pose the problem of categorizing 3D polygon meshes as learning appearance evolution from multi-view 2D images. Given a corpus of 3D polygon meshes, we first render the corresponding RGB and depth images from multiple viewpoints on a uniform sphere. Using rank pooling, we propose two methods...
3D face recognition holds great promise in achieving robustness to pose, expressions and occlusions. However, 3D face recognition algorithms are still far behind their 2D counterparts due to the lack of large-scale datasets. We present a model based algorithm for 3D face recognition and test its performance by combining two large public datasets of 3D faces. We propose a Fully Convolutional Deep Network...
Existing RGB-D object recognition methods either use channel specific handcrafted features, or learn features with deep networks. The former lack representation ability while the latter require large amounts of training data and learning time. In real-time robotics applications involving RGB-D sensors, we do not have the luxury of both. In this paper, we propose Localized Deep Extreme Learning Machines...
This paper addresses the problem of person-independent 4D facial expression recognition. Unlike the majority of existing works, we propose to extract spatio-temporal features in 4D data (3D expression sequences changing over time) to represent 3D facial expression dynamics sufficiently, rather than extracting features frame-by-frame. First, the proposed method extracts local depth patch-sequences...
Understanding the features employed by the human visual system in gender classification is considered a critical step towards improving machine based gender classification systems. We propose the use of 3D Euclidean and geodesic distances between biologically significant facial landmarks to classify gender. We perform five different experiments on the BU-3DFE face database to look for more representative...
We propose an action classification algorithm which uses Locality-constrained Linear Coding (LLC) to capture discriminative information of human body variations in each spatio-temporal subsequence of a video sequence. Our proposed method divides the input video into equally spaced overlapping spatio-temporal sub sequences, each of which is decomposed into blocks and then cells. We use the Histogram...
We propose an algorithm which combines the discriminative information from depth images as well as from 3D joint positions to achieve high action recognition accuracy. To avoid the suppression of subtle discriminative information and also to handle local occlusions, we compute a vector of many independent local features. Each feature encodes spatiotemporal variations of depth and depth gradients at...
Facial expressions form a significant part of our nonverbal communications and understanding them is essential for effective human computer interaction. Due to the diversity of facial geometry and expressions, automatic expression recognition is a challenging task. This paper deals with the problem of person-independent facial expression recognition from a single 3D scan. We consider only the 3D shape...
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