In general, biometry-based control systems may not rely on individual expected behavior or cooperation to operate appropriately. Instead, such systems should be aware of malicious procedures for unauthorized access attempts. Some works available in the literature suggest addressing the problem through gait recognition approaches. Such methods aim at identifying human beings through intrinsic perceptible features, despite dressed clothes or accessories. Although the issue denotes a relatively long-time challenge, most of the techniques developed to handle the problem present several drawbacks related to feature extraction and low classification rates, among other issues. However, deep learning-based approaches recently emerged as a robust set of tools to deal with virtually any image and computer-vision related problem, providing paramount results for gait recognition as well. Therefore, this work provides a surveyed compilation of recent works regarding biometric detection through gait recognition with a focus on deep learning approaches, emphasizing their benefits, and exposing their weaknesses. Besides, it also presents categorized and characterized descriptions of the datasets, approaches, and architectures employed to tackle associated constraints.
翻译:一般而言,生物测量控制系统可能并不依靠个人预期的行为或合作来适当运作,相反,这种系统应当了解未经授权访问的恶意程序。文献中的一些著作建议通过动作识别方法来解决这一问题。这些方法的目的是通过内在的可见特征识别人,尽管有穿戴衣装或配件。尽管这个问题代表了相对长期的挑战,但为处理这一问题而开发的大多数技术都存在与特征提取和低分类率等有关的若干缺陷。然而,基于深层次学习的方法最近出现了一套强有力的工具,用以处理几乎所有与图像和计算机观点有关的问题,为游戏识别提供了最重要的结果。因此,这项工作通过以深层学习方法为重点,通过文字识别,突出其好处,并暴露其弱点,从而对最近有关生物鉴别的工作进行了调查汇编,重点是深度学习方法,强调其益处,并揭示其弱点。此外,它还对数据集、方法和结构进行了分类和定性描述,以解决相关的制约因素。