  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
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 \textbf{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.
