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Identifying perceived emotions from people's walking style

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.

[...] The approach first extracts a person's walking gait from an RGB video of them walking, representing it as a series of 3-D poses. Subsequently, the researchers used a long short-term memory (LSTM) recurrent neural network and a random forest (RF) classifier to analyze these poses and identify the most prominent emotion felt by the person in the video, choosing between happiness, sadness, anger or neutral.

The LSTM is initially trained on a series of deep features, but these are later combined with affective features computed from the gaits using posture and movement cues. All of these features are ultimately classified using the RF classifier.

Randhavane and his colleagues carried out a series of preliminary tests on a dataset containing videos of people walking and found that their model could identify the perceived emotions of individuals with 80 percent accuracy. In addition, their approach led to an improvement of approximately 14 percent over other perceived emotion recognition methods that focus on people's walking style.

Original Submission