Which joint interactions in the human gait cycle can be used as biometric characteristics? Most current methods on gait recognition suffer from the lack of interpretability. We propose an interpretable feature representation of gait sequences by the graphical Granger causal inference. Gait sequence of a person in the standardized motion capture format, constituting a set of 3D joint spatial trajectories, is envisaged as a causal system of joints interacting in time. We apply the graphical Granger model (GGM) to obtain the so-called Granger causal graph among joints as a discriminative and visually interpretable representation of a person's gait. We evaluate eleven distance functions in the GGM feature space by established classification and class-separability evaluation metrics. Our experiments indicate that, depending on the metric, the most appropriate distance functions for the GGM are the total norm distance and the Ky-Fan 1-norm distance. Experiments also show that the GGM is able to detect the most discriminative joint interactions and that it outperforms five related interpretable models in correct classification rate and in Davies-Bouldin index. The proposed GGM model can serve as a complementary tool for gait analysis in kinesiology or for gait recognition in video surveillance.
翻译:人类行为周期中的哪些联合互动可用作生物鉴别特征?关于行为识别的多数现行方法缺乏解释性;我们建议用图形Granger因果推断法来解释行踪序列的可解释性特征。标准动作捕捉格式中的一个人的格伊特序列,构成一套3D联合空间轨迹,被设想为一个因果系统,即结合时间相互作用的因果系统。我们使用图形Granger模型(GGGM)来获取所谓的“Ganger”因果图,作为对某人行踪进行区分和直观解释的演示。我们通过既定的分类和分类分离性评价指标来评估GGGGM地貌空间的11个远程功能。我们的实验表明,根据测量标准,GGGM的最适当的距离功能是总标准距离和Ky-Fan 1-Norm距离。实验还表明,GGGM能够检测到最有歧视的联合互动,它超越了在正确的分类率和Davies-Boul索引中五个相关的可解释模型。拟议中的GGGM模型可用作用于监控模型或图像识别的一种补充工具。