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Second, to reason from the PAS images, we introduceĬontext infusion (Cont-In) blocks. This allows us to exploit body pose in addition to facialĬontext whenever available. Key regions from the input image using a mask generated from both body pose andįacial landmarks. We create part aware spatial (PAS) images by extracting First, we demonstrate our method, PERI, to leverage both body poseĪnd facial landmarks. Pose for emotion recognition, such methods fail to realize the potential thatįacial expressions, when available, offer. Person crops as well as the surrounding scene context. To address this, in the wild emotion recognition exploits full-body The face lacks pixel resolution and contains artifacts due to occlusion andīlur. Unreliable in natural unconstrained scenarios, such as in crowded scenes, as
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Methods rely primarily on facial features. To leverage the correlationīetween facial expression and the emotional state of a person, pioneering On emotion recognition using visual features. On various inputs including audio, visual, and textual cues. Emotion recognition aims to interpret the emotional states of a person based
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