A full-body layered deformable model for automatic model-based gait recognition
EURASIP Journal on Advances in Signal Processing
Combination of accumulated motion and color segmentation for human activity analysis
Journal on Image and Video Processing - Anthropocentric Video Analysis: Tools and Applications
Boosting discriminant learners for gait recognition using MPCA features
Journal on Image and Video Processing - Special issue on video-based modeling, analysis, and recognition of human motion
Gait recognition without subject cooperation
Pattern Recognition Letters
Partitioning gait cycles adaptive to fluctuating periods and bad silhouettes
ICB'07 Proceedings of the 2007 international conference on Advances in Biometrics
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In this paper, a layered deformable model (LDM) is proposed for human body pose recovery in gait analysis. This model is inspired by the manually labeled silhouettes in [6] and it is designed to closely match them. For frontoparallel gait, the introduced LDM model defines the body part widths and lengths, the position and the joint angles of human body using 22 parameters. The model consists of four layers and allows for limb deformation. With this model, our objective is to recover its parameters (and thus the human body pose) from automatically extracted silhouettes. LDM recovery algorithm is first developed for manual silhouettes, in order to generate ground truth sequences for comparison and useful statistics regarding the LDM parameters. It is then extended for automatically extracted silhouettes. The proposed methodologies have been tested on 10005 frames from 285 gait sequences captured under various conditions and an average error rate of 7% is achieved for the lower limb joint angles of all the frames, showing great potential for model-based gait recognition.