歩行スタイルから感情を識別するデータ駆動モデル
Identifying Emotions from Walking using Affective and Deep Features
歩行映像から3D姿勢を抽出し、LSTMの深層特徴と姿勢・動作の感情特徴を組み合わせて、歩き方から喜び・悲しみ・怒り・中立の感情を80%の精度で識別する手法を提案した論文。
著者: Tanmay Randhavane, Uttaran Bhattacharya, Kyra Kapsaskis, Kurt Gray, Aniket Bera, Dinesh Manocha
分類: cs.CV, cs.RO
原文アブストラクト
We present a new data-driven model and algorithm to identify the perceived emotions of individuals based on their walking styles. Given an RGB video of an individual walking, we extract his/her walking gait in the form of a series of 3D poses. Our goal is to exploit the gait features to classify the emotional state of the human into one of four emotions: happy, sad, angry, or neutral. Our perceived emotion recognition approach uses deep features learned via LSTM on labeled emotion datasets. Furthermore, we combine these features with affective features computed from gaits using posture and movement cues. These features are classified using a Random Forest Classifier. We show that our mapping between the combined feature space and the perceived emotional state provides 80.07% accuracy in identifying the perceived emotions. In addition to classifying discrete categories of emotions, our algorithm also predicts the values of perceived valence and arousal from gaits. We also present an EWalk (Emotion Walk) dataset that consists of videos of walking individuals with gaits and labeled emotions. To the best of our knowledge, this is the first gait-based model to identify perceived emotions from videos of walking individuals.
