Object recognition by computer: the role of geometric constraints
Object recognition by computer: the role of geometric constraints
Moving object recognition in eigenspace representation: gait analysis and lip reading
Pattern Recognition Letters
Predicting Performance of Object Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Individual recognition from periodic activity using hidden Markov models
HUMO '00 Proceedings of the Workshop on Human Motion (HUMO'00)
Tracking of Persons in Monocular Image Sequences
NAM '97 Proceedings of the 1997 IEEE Workshop on Motion of Non-Rigid and Articulated Objects (NAM '97)
Automatic gait recognition via statistical approaches for extendedtemplate features
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Individual Recognition Using Gait Energy Image
IEEE Transactions on Pattern Analysis and Machine Intelligence
Computing and evaluating view-normalized body part trajectories
Image and Vision Computing
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Existing gait recognition approaches do not give their theoretical or experimental performance predictions. Therefore, the discriminating power of gait as a feature for human recognition cannot be evaluated. In this paper, we first propose a kinematic-based approach to recognize human by gait. The proposed approach estimates 3D human walking parameters by performing a least squares fit of the 3D kinematic model to the 2D silhouette extracted from a monocular image sequence. Next, a Bayesian-based statistical analysis is performed to evaluate the discriminating power of extracted stationary gait features. Through probabilistic simulation, we not only predict the probability of correct recognition (PCR) with regard to different within-class feature variance, but also obtain the upper bound on PCR with regard to different human silhouette resolution. In addition, the maximum number of people in a database is obtained given the allowable error rate. This is extremely important for gait recognition in large databases.