Regional Attention Network (RAN) for Head Pose and Fine-grained Gesture Recognition


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6 Citations (Scopus)
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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 languageEnglish
Pages (from-to)1-1
Number of pages1
JournalIEEE Transactions on Affective Computing
Early online date16 Oct 2020
Publication statusPublished - 16 Oct 2020


  • 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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