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The algorithm identifies the perceived emotions of individuals based on their walking styles. Given an RGB video of an individual walking (top), the researchers’ method extracts his/her walking gait as a series of 3D poses (bottom). It then uses a combination of deep features learned via an LSTM and affective features computed using posture and movement cues and classify these into basic emotions (e.g., happy, sad, etc.), using a Random Forest Classifier. Credit: Randhavane et al.
A team of researchers at the University of North Carolina at Chapel Hill and the University of Maryland at College Park has recently developed a new deep learning model that can identify people's emotions based on their walking styles. Their approach, outlined in a paper pre-published on arXiv, works by extracting an individual's gait from an RGB video of him/her walking, then analyzing it and classifying it as one of four emotions: happy, sad, angry or neutral.
Emotions - Role - Lives - Experiences - World
"Emotions play a significant role in our lives, defining our experiences, and shaping how we view the world and interact with other humans," Tanmay Randhavane, one of the primary researchers and a graduate student at UNC, told TechXplore. "Perceiving the emotions of other people helps us understand their behavior and decide our actions toward them. For example, people communicate very differently with someone they perceive to be angry and hostile than they do with someone they perceive to be calm and contented."
Most existing emotion recognition and identification tools work by analyzing facial expressions or voice recordings. However, past studies suggest that body language (e.g., posture, movements, etc.) can also say a lot about how someone is feeling. Inspired by these observations, the researchers set out to develop a tool that can automatically identify the perceived emotion of individuals based on their walking style.
Advantage - Emotion
"The main advantage of our perceived emotion...
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