The FERET Evaluation Methodology for Face-Recognition Algorithms
IEEE Transactions on Pattern Analysis and Machine Intelligence
The Gait Identification Challenge Problem: Data Sets and Baseline Algorithm
ICPR '02 Proceedings of the 16 th International Conference on Pattern Recognition (ICPR'02) Volume 1 - Volume 1
Automatic extraction and description of human gait models for recognition purposes
Computer Vision and Image Understanding
Automatic gait recognition by symmetry analysis
Pattern Recognition Letters - Special issue: Audio- and video-based biometric person authentication (AVBPA 2001)
Gait-Based Recognition of Humans Using Continuous HMMs
FGR '02 Proceedings of the Fifth IEEE International Conference on Automatic Face and Gesture Recognition
Extracting Human Gait Signatures by Body Segment Properties
SSIAI '02 Proceedings of the Fifth IEEE Southwest Symposium on Image Analysis and Interpretation
Silhouette Analysis-Based Gait Recognition for Human Identification
IEEE Transactions on Pattern Analysis and Machine Intelligence
Simplest Representation Yet for Gait Recognition: Averaged Silhouette
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 4 - Volume 04
Combining multiple evidences for gait recognition
ICME '03 Proceedings of the 2003 International Conference on Multimedia and Expo - Volume 3 (ICME '03) - Volume 03
Modelling the effects of walking speed on appearance-based gait recognition
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Human gait recognition via deterministic learning
Neural Networks
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Existing methods of gait recognition are mostly based on either holistic shape information or kinematics features. Both of them are very important cues in human gait recognition. In this paper we propose a novel method via fusing shape and motion features. Firstly, the binary silhouette of a walking person is detected from each frame of the monocular image sequences. Then the static shape is represented using the ratio of the body’s height to width and the pixel number of silhouette. Meanwhile, a 2D stick figure model and trajectory-based kinematics features are extracted from the image sequences for describing and analyzing the gait motion. Next, we discuss two fusion strategies relevant to the above mentioned feature sets: feature level fusion and decision level fusion. Finally, a similarity measurement based on the gait cycles and two different classifiers (Nearest Neighbor and KNN) are carried out to recognize different subjects. Experimental results on UCSD and CMU databases demonstrate the feasibility of the proposed algorithm and show that fusion can be an effective strategy to improve the recognition performance.