Gait recognition using dynamic texture descriptors

The human motion analysis is an attractive topic in biometric research. Common biometrics is usually time-consuming, limited and collaborative. These drawbacks pose major challenges to recognition process. Recent researches indicate people have considerable ability to recognize others by their natural walking. Therefore, gait recognition has obtained great tendency in biometric systems. Gait analysis is inconspicuous, needs no contact, cannot be hidden and is evaluated at distance. This paper presents a bag of word method for gait recognition based on dynamic textures. Dynamic textures combine appearance and motion information. Since human walking has statistical variations in both spatial and temporal space, it can be described with dynamic texture features. To obtain these features, we extract spatiotemporal interest points and describe them by a dynamic texture descriptor. To get more suitable results, we extend LBP-TOP as a rotation invariant dynamic texture descriptor. Afterwards, hierarchical K-means algorithm is employed to map features into visual words. At result, human walking represent as a histogram of video-words occurrences. We evaluate the performance of our method on two dataset: the KTH dataset and IXMAS multiview dataset.