This paper explores in parallel the underlying mechanisms in human perception of biological motion and the best approaches for automatic classification of gait. The experiments tested three different learning paradigms, namely, biological, biomimetic, and non-biomimetic models for gender identification from human gait. Psychophysical experiments with twenty-one observers were conducted along with computational experiments without applying any gender specific modifications to the models or the stimuli. Results demonstrate the utilization of a generic memory based learning system in humans for gait perception, thus reducing ambiguity between two opposing learning systems proposed for biological motion perception. Results also support the biomimetic nature of memory based artificial neural networks (ANN) in their ability to emulate biological neural networks, as opposed to non-biomimetic models. In addition, the comparison between biological and computational learning approaches establishes a memory based biomimetic model as the best candidate for a generic artificial gait classifier (83% accuracy, p < 0.001), compared to human observers (66%, p < 0.005) or non-biomimetic models (83%, p < 0.001) while adhering to human-like sensitivity to gender identification, promising potential for application of the model in any given non-gender based gait perception objective with superhuman performance.

中文翻译：

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步态的性别认知：生物、仿生和非仿生学习范式的比较

本文同时探讨了人类感知生物运动的潜在机制和步态自动分类的最佳方法。实验测试了三种不同的学习范式，即用于从人类步态进行性别识别的生物、仿生和非仿生模型。对 21 名观察者进行的心理物理实验与计算实验一起进行，没有对模型或刺激进行任何性别特定的修改。结果表明，人类使用基于通用记忆的学习系统进行步态感知，从而减少了为生物运动感知提出的两个对立学习系统之间的歧义。结果还支持基于记忆的人工神经网络 (ANN) 在模拟生物神经网络的能力方面的仿生性质，而不是非仿生模型。此外，生物和计算学习方法之间的比较建立了基于记忆的仿生模型作为通用人工步态分类器的最佳候选者（83% 准确度，p < 0.001），与人类观察者（66%，p < 0.005）或非仿生模型 (83%, p < 0.001) 同时坚持人类对性别识别的敏感性，有望将该模型应用于任何给定的非基于性别的步态感知目标，并具有超人的表现。