Date of this Version
Our goal is to establish a simple baseline method for human identification based on body shape and gait. This baseline recognition method provides a lower bound against which to evaluate more complicated procedures. We present a viewpoint dependent technique based on template matching of body silhouettes. Cyclic gait analysis is performed to extract key frames from a test sequence. These frames are compared to training frames using normalized correlation, and subject classification is performed by nearest neighbor matching among correlation scores. The approach implicitly captures biometric shape cues such as body height, width, and body-part proportions, as well as gait cues such as stride length and amount of arm swing. We evaluate the method on four databases with varying viewing angles, background conditions (indoors and outdoors), walk styles and pixels on target.
biometrics (access control), computer vision, correlation methods, gait analysis, image classification, image matching, image motion analysis, image sequences, shape measurement, video databases, arm swing, background conditions, baseline recognition method, biometric shape cues, body height, body shape, body width, body-part proportions, correlation scores, cyclic gait analysis, databases, gait cues, indoor conditions, key frame extraction, lower bound, nearest-neighbor matching, normalized correlation, outdoor conditions, pixels, silhouette-based human identification, stride length, subject classification, template matching, test image sequence, training frames, viewing angles, viewpoint-dependent technique, walking styles
Date Posted: 30 April 2005