  Affective computing plays a key role in human-computer interactions,
entertainment, teaching, safe driving, and multimedia integration. Major
breakthroughs have been made recently in the areas of affective computing
(i.e., emotion recognition and sentiment analysis). Affective computing is
realized based on unimodal or multimodal data, primarily consisting of physical
information (e.g., textual, audio, and visual data) and physiological signals
(e.g., EEG and ECG signals). Physical-based affect recognition caters to more
researchers due to multiple public databases. However, it is hard to reveal
one's inner emotion hidden purposely from facial expressions, audio tones, body
gestures, etc. Physiological signals can generate more precise and reliable
emotional results; yet, the difficulty in acquiring physiological signals also
hinders their practical application. Thus, the fusion of physical information
and physiological signals can provide useful features of emotional states and
lead to higher accuracy. Instead of focusing on one specific field of affective
analysis, we systematically review recent advances in the affective computing,
and taxonomize unimodal affect recognition as well as multimodal affective
analysis. Firstly, we introduce two typical emotion models followed by commonly
used databases for affective computing. Next, we survey and taxonomize
state-of-the-art unimodal affect recognition and multimodal affective analysis
in terms of their detailed architectures and performances. Finally, we discuss
some important aspects on affective computing and their applications and
conclude this review with an indication of the most promising future
directions, such as the establishment of baseline dataset, fusion strategies
for multimodal affective analysis, and unsupervised learning models.
