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The identification of facial expressions with human emotions plays a key role in non-verbal human communication and has applications in several areas. In this work, we analyze two main approaches for expression recognition.
This paper proposes three simple, compact yet effective representations of depth sequences, referred to respectively as Dynamic Depth Images (DDI), Dynamic Depth Normal Images (DDNI) and Dynamic Depth Motion Normal Images (DDMNI). These dynamic images are constructed from a sequence of depth maps using bidirectional rank pooling to effectively capture the spatial-temporal information. Such image-based...
HMMs are widely used in action and gesture recognition due to their implementation simplicity, low computational requirement, scalability and high parallelism. They have worth performance even with a limited training set. All these characteristics are hard to find together in other even more accurate methods. In this paper, we propose a novel double-stage classification approach, based on Multiple...
This paper presents a high precision gesture recognition system that leverages the Doppler effect of ultrasound to sense in-air hand gestures. The system can precisely identify a wider variety of gestures than other systems without any modification to consumer laptops. The system recognizes quantitatively detailed and complex movements from the signals reflected by a moving body. A Hidden Markov Model...
In this paper, we tackle the continuous gesture recognition problem with a two streams Recurrent Neural Networks (2S-RNN) for the RGB-D data input. In our framework, the spotting-recognition strategy is used, that means the continuous gestures are first segmented into separated gestures, and then each isolated gesture is recognized by using the 2S-RNN. Concretely, the gesture segmentation is based...
This paper addresses the problem of continuous gesture recognition from sequences of depth maps using Convolutional Neural networks (ConvNets). The proposed method first segments individual gestures from a depth sequence based on quantity of movement (QOM). For each segmented gesture, an Improved Depth Motion Map (IDMM), which converts the depth sequence into one image, is constructed and fed to a...
The gesture recognition has raised attention in computer vision owing to its many applications. However, video-based large-scale gesture recognition still faces many challenges, since many factors like background may disturb the accuracy. To achieve gesture recognition with large-scale videos, we propose a method based on RGB-D data. To learn gesture details better, the inputs are expanded into 32-frame...
In this paper, we propose using 3D Convolutional Neural Networks for large scale user-independent continuous gesture recognition. We have trained an end-to-end deep network for continuous gesture recognition (jointly learning both the feature representation and the classifier). The network performs three-dimensional (i.e. space-time) convolutions to extract features related to both the appearance...
Human gesture recognition is one of the central research fields of computer vision, and effective gesture recognition is still challenging up to now. In this paper, we present a pyramidal 3D convolutional network framework for large-scale isolated human gesture recognition. 3D convolutional networks are utilized to learn the spatiotemporal features from gesture video files. Pyramid input is proposed...
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