Humans are adept at perceiving biological motion for purposes such as the discrimination of gender. Observers classify the gender of a walker at significantly above chance levels from a point-light distribution of joint trajectories. However, performance drops to chance level or below for vertically inverted stimuli, a phenomenon known as the inversion effect. This lack of robustness may reflect either a generic learning mechanism that has been exposed to insufficient instances of inverted stimuli or the activation of specialized mechanisms that are pre-tuned to upright stimuli. To address this issue, the authors compare the psychophysical performance of humans with the computational performance of neuromimetic machine-learning models in the classification of gender from gait by using the same biological motion stimulus set. Experimental results demonstrate significant similarities, which include those in the predominance of kinematic motion cues over structural cues in classification accuracy. Second, learning is expressed in the presence of the inversion effect in the models as in humans, suggesting that humans may use generic learning systems in the perception of biological motion in this task. Finally, modifications are applied to the model based on human perception, which mitigates the inversion effect and improves performance accuracy. The study proposes a paradigm for the investigation of human gender perception from gait and makes use of perceptual characteristics to develop a robust artificial gait classifier for potential applications such as clinical movement analysis.