Automatic gait recognition using area-based metrics
Pattern Recognition Letters
Silhouette Analysis-Based Gait Recognition for Human Identification
IEEE Transactions on Pattern Analysis and Machine Intelligence
Synchronization of oscillations for machine perception of gaits
Computer Vision and Image Understanding
Background Subtraction Using Markov Thresholds
WACV-MOTION '05 Proceedings of the IEEE Workshop on Motion and Video Computing (WACV/MOTION'05) - Volume 2 - Volume 02
Outdoor recognition at a distance by fusing gait and face
Image and Vision Computing
Gait recognition using image self-similarity
EURASIP Journal on Applied Signal Processing
Gait Recognition by Applying Multiple Projections and Kernel PCA
MLDM '07 Proceedings of the 5th international conference on Machine Learning and Data Mining in Pattern Recognition
Automatic Gait Recognition Using Weighted Binary Pattern on Video
AVSS '09 Proceedings of the 2009 Sixth IEEE International Conference on Advanced Video and Signal Based Surveillance
Synchronization of oscillations for machine perception of gaits
Computer Vision and Image Understanding
Improved gait recognition by multiple-projections normalization
Machine Vision and Applications
Radar micro-Doppler for long range front-view gait recognition
BTAS'09 Proceedings of the 3rd IEEE international conference on Biometrics: Theory, applications and systems
Gait shape estimation for identification
AVBPA'03 Proceedings of the 4th international conference on Audio- and video-based biometric person authentication
Gait recognition using active shape models
ACIVS'07 Proceedings of the 9th international conference on Advanced concepts for intelligent vision systems
What image information is important in silhouette-based gait recognition?
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
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
Advances in automatic gait recognition
FGR' 04 Proceedings of the Sixth IEEE international conference on Automatic face and gesture recognition
Gait style and gait content: bilinear models for gait recognition using gait re-sampling
FGR' 04 Proceedings of the Sixth IEEE international conference on Automatic face and gesture recognition
Human gait recognition using extraction and fusion of global motion features
Multimedia Tools and Applications
Appearance-Based gait recognition using independent component analysis
ICNC'06 Proceedings of the Second international conference on Advances in Natural Computation - Volume Part I
Automatic gait recognition by multi-projection analysis
IEA/AIE'06 Proceedings of the 19th international conference on Advances in Applied Artificial Intelligence: industrial, Engineering and Other Applications of Applied Intelligent Systems
Towards scalable view-invariant gait recognition: multilinear analysis for gait
AVBPA'05 Proceedings of the 5th international conference on Audio- and Video-Based Biometric Person Authentication
A new approach for human identification using gait recognition
ICCSA'06 Proceedings of the 2006 international conference on Computational Science and Its Applications - Volume Part III
A new attempt to silhouette-based gait recognition for human identification
AI'06 Proceedings of the 19th international conference on Advances in Artificial Intelligence: Canadian Society for Computational Studies of Intelligence
Human gait recognition via deterministic learning
Neural Networks
Feature subset selection applied to model-free gait recognition
Image and Vision Computing
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Identification of people from gait captured on video has become a challenge problem in computer vision. However, there is not a baseline algorithm or standard dataset for measuring, or determining what factors affect performance. In fact, the conditions under which the problem is ``solvable'' are not understood or characterized. This paper describes a large set of video sequences (about 300 GB of data related to 452 sequences from 74 subjects) acquired to investigate important dimensions of this problem, such as variations due to viewpoint, footwear, and walking surface. We introduce the HumanID challenge problem. The challenge problem contains a set of experiments of increasing difficulty, a baseline algorithm, and its performance on the challenge problem. Our results suggest that differences in footwear or walking surface type between the gallery and probe video sequence are factors that affect performance. The data set, the source code for the baseline algorithm, and UNIX scripts to reproduce the basic results reported here are available to the research community at {\small {\tt http://marathon.csee.usf.edu/GaitBaseline/}}