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HUMAN IDENTIFICATION BASED ON GAIT MODELING

机译:基于步态建模的人类识别

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摘要

Human gait is a dynamic biometrical feature which is complex and difficult to imitate. It is unique and more secure than static features such as passwords, fingerprints and facial features. In this paper, we present intelligent shoes for human identification based on human gait modeling and similarity evaluation with hidden Markov models (HMMs). Firstly we describe the intelligent shoe system for collecting human dynamic gait performance. Using the proposed machine learning method hidden Markov models, an individual wearer's gait model is derived and we then demonstrate the procedure for recognizing different wearers by analyzing the corresponding models. Next, we define a hidden-Markov-model-based similarity measure which allows us to evaluate resultant learning models. With the most likely performance criterion, it will help us to derive the similarity of individual behavior and its corresponding model. By utilizing human gait modeling and similarity evaluation based on hidden Markov models, the proposed method has produced satisfactory results for human identification during testing.
机译:人类步态是一种动态的生物特征,复杂且难以模仿。它比密码、指纹和面部特征等静态功能更独特且更安全。本文提出了基于人类步态建模和隐马尔可夫模型(HMMs)相似性评估的人类识别智能鞋。首先,我们描述了用于收集人体动态步态表现的智能鞋系统。使用所提出的隐马尔可夫模型的机器学习方法,推导了单个佩戴者的步态模型,然后通过分析相应的模型来演示识别不同佩戴者的过程。接下来,我们定义了一个基于隐藏马尔科夫模型的相似性度量,它使我们能够评估结果学习模型。有了最可能的表现标准,它将帮助我们推导出个体行为及其相应模型的相似性。通过利用基于隐马尔可夫模型的人体步态建模和相似性评估,在测试过程中对人类识别产生了满意的结果。

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