Abstract
Affect is often expressed via non-verbal body language such as actions/gestures, which are vital indicators for human behaviors. Recent studies on recognition of fine-grained actions/gestures in monocular images have mainly focused on modeling spatial configuration of body parts representing body pose, human-objects interactions and variations in local appearance. The results show that this is a brittle approach since it relies on the accurate body parts/objects detection. In this work, we argue that there exist local discriminative semantic regions, whose “informativeness” can be evaluated by the attention mechanism for inferring fine-grained gestures/actions. To this end, we propose a novel end-to-end Regional Attention Network (RAN), which is a fully Convolutional Neural Network (CNN) to combine multiple contextual regions through attention mechanism, focusing on parts of the images that are most relevant to a given task. Our regions consist of one or more consecutive cells and are adapted from the strategies used in computing HOG (Histogram of Oriented Gradient) descriptor. The model is extensively evaluated on ten datasets belonging to 3 different scenarios: 1) head pose recognition, 2) drivers state recognition, and 3) human action and facial expression recognition. The proposed approach outperforms the state-of-the-art by a considerable margin in different metrics.
Original language | English |
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Pages (from-to) | 1-1 |
Number of pages | 1 |
Journal | IEEE Transactions on Affective Computing |
Early online date | 16 Oct 2020 |
DOIs | |
Publication status | Published - 16 Oct 2020 |
Keywords
- Gesture Recognition
- Head Pose Recognition
- Facial Expressions Recognition
- Attention Mechanism
- Fine-grained Gesture Recognition
- Computer Vision
- Human-Object Interaction
- Regional Attention Network
- Convolutional Neural Network
- Head
- Image recognition
- Face recognition
- Pose estimation
- Magnetic heads
- Task analysis
- Annotations
Research Institutes
- Health Research Institute
Research Centres
- Centre for Intelligent Visual Computing Research
- Data Science STEM Research Centre
- Data and Complex Systems Research Centre
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ARDHENDU BEHERA, PhD
- Computer Science - Professor of Computer Vision and AI
- Health Research Institute
Person: Research institute member, Academic