Evaluation of gait recognition using dynamics of feature points and local shape features under clothing variation conditions

Abstract

 STAGE DOI: 10.3408 / jafst.745 ) Gait recognition is one of recently evolving techniques by which we can recog-nize individuals by one's gait. There are two major approaches; silhouette-based and model-based. In Japan, a method based on GEI ( Gait Energy Image ) , which is one of the silhouette-based approaches, is used for forensic purposes. Sometimes, it is a problem of silhouettes' variabilities in one person due to diŠerent clothing that les-sen recognition reliability under the GEI method. Here, we analyzed and evaluated the average error rates under clothing variation conditions using the method called Dynamic-features method, which we previously proposed. The Dynamic-features method was built inspired by previous studies of model-based gait recognition, which uses time-series of feature points and local shape features around the points automatically extracted from silhouette sequences. Before analysis, we roughly categorize whole data in the OU-ISIR gait database -treadmill dataset B-, which con-tains side-view data, into ˆve clothing categories in order to deal with realistic oŠ-line forensic situation, where we cannot strictly control utilization of dynamic properties of human's gait.