Human observers readily recognize emotions expressed in body movement. Their perceptual judgments are based on simple movement features, such as overall speed, but also on more intricate posture and dynamic cues. The systematic analysis of such features is complicated due to the difficulty of considering the large number of potentially relevant kinematic and dynamic parameters. To identify emotion-specific features we motion-captured the neutral and emotionally expressive (anger, happiness, sadness, fear) gaits of 25 individuals. Body posture was characterized by average flexion angles, and a low-dimensional parameterization of the spatio-temporal structure of joint trajectories was obtained by approximation with a nonlinear mixture model. Applying sparse regression, we extracted critical emotion-specific posture and movement features, which typically depended only on a small number of joints. The features we extracted from the motor behavior closely resembled features that were critical for the perception of emotion from gait, determined by a statistical analysis of classification and rating judgments of 21 observers presented with avatars animated with the recorded movements. The perceptual relevance of these features was further supported by another experiment showing that artificial walkers containing only the critical features induced high-level after-effects matching those induced by adaptation with natural emotional walkers.