  Emotion recognition aims to interpret the emotional states of a person based
on various inputs including audio, visual, and textual cues. This paper focuses
on emotion recognition using visual features. To leverage the correlation
between facial expression and the emotional state of a person, pioneering
methods rely primarily on facial features. However, facial features are often
unreliable in natural unconstrained scenarios, such as in crowded scenes, as
the face lacks pixel resolution and contains artifacts due to occlusion and
blur. To address this, in the wild emotion recognition exploits full-body
person crops as well as the surrounding scene context. In a bid to use body
pose for emotion recognition, such methods fail to realize the potential that
facial expressions, when available, offer. Thus, the aim of this paper is
two-fold. First, we demonstrate our method, PERI, to leverage both body pose
and facial landmarks. We create part aware spatial (PAS) images by extracting
key regions from the input image using a mask generated from both body pose and
facial landmarks. This allows us to exploit body pose in addition to facial
context whenever available. Second, to reason from the PAS images, we introduce
context infusion (Cont-In) blocks. These blocks attend to part-specific
information, and pass them onto the intermediate features of an emotion
recognition network. Our approach is conceptually simple and can be applied to
any existing emotion recognition method. We provide our results on the publicly
available in the wild EMOTIC dataset. Compared to existing methods, PERI
achieves superior performance and leads to significant improvements in the mAP
of emotion categories, while decreasing Valence, Arousal and Dominance errors.
Importantly, we observe that our method improves performance in both images
with fully visible faces as well as in images with occluded or blurred faces.
