PDF(Pubmed)
Abstract：
Gait recognition has become an increasingly promising area of research in the search for noninvasive and effective methods of person identification. Its potential applications in security systems and medical diagnosis make it an exciting field with wide-ranging implications. However, precisely recognizing and assessing gait patterns is difficult, particularly in changing situations or from multiple perspectives. In this study, we utilized the widely used CASIA-B dataset to observe the performance of our proposed gait recognition model, with the aim of addressing some of the existing limitations in this field. Fifty individuals are randomly selected from the dataset, and the resulting data are split evenly for training and testing purposes. We begin by excerpting features from gait photos using two well-known deep learning networks, MobileNetV1 and Xception. We then combined these features and reduced their dimensionality via principal component analysis (PCA) to improve the model\'s performance. We subsequently assessed the model using two distinct classifiers: a random forest and a one against all support vector machine (OaA-SVM). The findings indicate that the OaA-SVM classifier manifests superior performance compared to the others, with a mean accuracy of 98.77% over eleven different viewing angles. This study is conducive to the development of effective gait recognition algorithms that can be applied to heighten people\'s security and promote their well-being.
摘要：
在寻找非侵入性和有效的人员识别方法中，步态识别已成为越来越有前途的研究领域。它在安全系统和医疗诊断中的潜在应用使其成为一个具有广泛影响的令人兴奋的领域。然而,精确识别和评估步态模式是困难的，特别是在不断变化的情况下或从多个角度来看。在这项研究中,我们利用广泛使用的CASIA-B数据集来观察我们提出的步态识别模型的性能，目的是解决这一领域现有的一些限制。从数据集中随机选择50个人，并将生成的数据平均分配用于训练和测试目的。我们首先使用两个著名的深度学习网络从步态照片中摘录特征，MobileNetV1和Xception。然后，我们将这些特征结合起来，并通过主成分分析（PCA）降低了它们的维数，以提高模型的性能。我们随后使用两个不同的分类器评估模型：一个随机森林和一个针对所有支持向量机（OaA-SVM）。研究结果表明，与其他分类器相比，OaA-SVM分类器表现出优异的性能，在11个不同视角下的平均准确度为98.77%。本研究有助于开发有效的步态识别算法,提高人们的安全水平,促进人们的幸福感。
