Gait Recognition: Databases, Representations, and Applications

Abstract

There has been a considerable progress in automatic recognition of people by the way they walk since its inception almost 20 years ago: There is now a plethora of techniques and data that continue to show that a person's gait is indeed unique. Gait recognition is a behavioral biometric that is available even at a distance from a camera when other biometrics may be occluded, obscured, or suffering from insufficient image resolution (e.g., a blurred face image or a face image occluded by mask). Since gait recognition does not require subject cooperation due to its noninvasive capturing process, it is expected to be applied for criminal investigation from CCTV footages in public and private places. This article introduces current progress, a research background, and basic approaches for gait recognition in the first three sections, and two important aspects of gait recognition, the gait databases and gait feature representations, are described in the following sections.Publicly available gait databases are essential for benchmarking individual approaches, and such databases should contain a sufficient number of subjects as well as covariate factors to realize statistically reliable performance evaluation and also robust gait recognition. Gait recognition researchers have therefore built such useful gait databases that incorporate subject diversities and/or rich covariate factors.Gait feature representation is also an important aspect for effective and efficient gait recognition. We describe the two main approaches to representation: model‐free (appearance‐based) approaches and model‐based approaches. In particular, silhouette‐based model‐free approaches predominate in recent studies and many have been proposed and are described in detail.Performance evaluation results of such recent gait feature representations on two of the publicly available gait databases are reported: USF Human ID with rich covariate factors such as views, surface, bag, shoes, and time elapse and OU‐ISIR LP with more than 4000 subjects. Since gait recognition is suitable for criminal investigation, applications of the gait recognition to forensics are addressed with real criminal cases in the application section. Finally, several open problems of the gait recognition are discussed to show future research avenues of the gait recognition.