Gait or walking pattern has been known as one of the alternative biometric solutions used in surveillance monitoring and control. The methods of gait recognition have been developed for decades using various techniques in different concepts. This study is a review paper collecting gait recognition approaches in both perspectives of model-based approaches relying on key joints/parts of the human body and appearance-based approaches relying on gait silhouettes. The existing methods addressing one of the most important real-world challenges, i.e. view changes, are emphasised and summarised in this study. Also, recent methods based on convolutional neural network solving the gait recognition and their challenges of view changes are illustrated. In addition, the publicly-available gait datasets and corresponding recognition performance and comparison are concluded in each section. The state-of-the-art gait recognition methods can achieve up to a perfect score of 100% accuracy for the normal walking, and above 80% in average for the view changes ranging from 0° to 180°.

中文翻译：

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回顾步态识别方法及其对视野变化的挑战

步态或步行模式已被公认为监视监视和控制中使用的替代生物识别解决方案之一。步态识别方法已经使用不同概念的各种技术开发了数十年。这项研究是一篇综述论文，从基于人体关键关节/部位的基于模型的方法和依赖于步态轮廓的基于外观的方法两个方面收集了步态识别方法。这项研究强调并总结了解决现实世界中最重要挑战之一的现有方法。此外，还展示了基于卷积神经网络解决步态识别的最新方法及其对视图变化的挑战。此外，每个部分总结了公开的步态数据集以及相应的识别性能和比较。最先进的步态识别方法可在正常行走中获得高达100％准确度的完美评分，对于从0°到180°的视角变化，平均评分可达到80％以上。