This paper presents a gait recognition system that uses recurrent neural networks (RNNs) and support vector machines (SVMs) for identifying individuals. Our system extracts the spatiotemporal features of distances between the waist and various joint positions obtained by a Kinect sensor. These spatiotemporal features are invariant for a walking subject. To verify our system performance, we conducted tests using the data of 12 individuals. The data were divided into two datasets for training and testing. The RNNs and SVMs were trained for classification using the training dataset. SVMs achieved an average accuracy of over 99% for the test dataset, whereas the average accuracy of RNNs was 94%
