An interpretable gait recognition method based on time series features

Abstract

Gait recognition is a type of biometric recognition that can be used as an identification tool in various applications. Deep learning-based methods have recently exhibited promising accuracy in gait recognition tasks; however, in addition to an accurate prediction, these methods are required to explain the recognition results. The black-box nature of deep neural networks makes it very difficult to interpret the basis for their identification. The published studies on the interpretability of gait recognition are also in a blank state. Moreover, deep neural networks require a large amount of data to learn the model parameters and an effective generalization on unseen data is difficult when the problem size is small. Thus, this paper presents a gait recognition method combining accuracy and interpretability. The gait feature is represented as a multi-dimensional time series and a Shapelet-based time series classification method is used for gait recognition. A Shapelet is the most discriminative subsequence in time series that makes the proposed method provide interpretability and accuracy simultaneously. We conducted experiments on the CASIA-B dataset and compared the proposed method with several state-of-the-arts deep learning methods. Experiments show that the proposed method can provide an accuracy close to that of deep neural networks on small-scale data sets. At the same time, the decision-making reason of the model can be explained in detail. Concretely, our method can reveal discriminative gait features and frame numbers for specific subjects.