Using Gait as a Biometric, via Phase-weighted Magnitude Spectra
AVBPA '97 Proceedings of the First International Conference on Audio- and Video-Based Biometric Person Authentication
EigenGait: Motion-Based Recognition of People Using Image Self-Similarity
AVBPA '01 Proceedings of the Third International Conference on Audio- and Video-Based Biometric Person Authentication
A Multi-view Method for Gait Recognition Using Static Body Parameters
AVBPA '01 Proceedings of the Third International Conference on Audio- and Video-Based Biometric Person Authentication
Phase in model-free perception of gait
HUMO '00 Proceedings of the Workshop on Human Motion (HUMO'00)
Phase in model-free perception of gait
HUMO '00 Proceedings of the Workshop on Human Motion (HUMO'00)
On the Relationship of Human Walking and Running: Automatic Person Identification by Gait
ICPR '02 Proceedings of the 16 th International Conference on Pattern Recognition (ICPR'02) Volume 1 - Volume 1
Stride and Cadence as a Biometric in Automatic Person Identification and Verification
FGR '02 Proceedings of the Fifth IEEE International Conference on Automatic Face and Gesture Recognition
Silhouette-Based Human Identification from Body Shape and Gait
FGR '02 Proceedings of the Fifth IEEE International Conference on Automatic Face and Gesture Recognition
Motion-Based Recognition of People in EigenGait Space
FGR '02 Proceedings of the Fifth IEEE International Conference on Automatic Face and Gesture Recognition
Baseline Results for the Challenge Problem of Human ID Using Gait Analysis
FGR '02 Proceedings of the Fifth IEEE International Conference on Automatic Face and Gesture Recognition
Gait Analysis for Recognition and Classification
FGR '02 Proceedings of the Fifth IEEE International Conference on Automatic Face and Gesture Recognition
Silhouette Analysis-Based Gait Recognition for Human Identification
IEEE Transactions on Pattern Analysis and Machine Intelligence
Individual Recognition Using Gait Energy Image
IEEE Transactions on Pattern Analysis and Machine Intelligence
Gait Tracking and Recognition Using Person-Dependent Dynamic Shape Model
FGR '06 Proceedings of the 7th International Conference on Automatic Face and Gesture Recognition
Human Gait Tracking Based on Linear Model Fitting
IMSCCS '06 Proceedings of the First International Multi-Symposiums on Computer and Computational Sciences - Volume 1 (IMSCCS'06) - Volume 01
Efficient Night Gait Recognition Based on Template Matching
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 03
Gait recognition using wavelet descriptors and independent component analysis
ISNN'06 Proceedings of the Third international conference on Advnaces in Neural Networks - Volume Part II
Gait recognition using principal curves and neural networks
ISNN'06 Proceedings of the Third international conference on Advnaces in Neural Networks - Volume Part II
Automatic gait recognition based on statistical shape analysis
IEEE Transactions on Image Processing
Uniprojective features for gait recognition
ICB'07 Proceedings of the 2007 international conference on Advances in Biometrics
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Human identification by recognizing the spontaneous gait recorded in real-world setting is a tough and not yet fully resolved problem in biometrics research. Several issues have contributed to the difficulties of this task. They include various poses, different clothes, moderate to large changes of normal walking manner due to carrying diverse goods when walking, and the uncertainty of the environments where the people are walking. In order to achieve a better gait recognition, this paper proposes a new method based on Weighted Binary Pattern (WBP). WBP first constructs binary pattern from a sequence of aligned silhouettes. Then, adaptive weighting technique is applied to discriminate significances of the bits in gait signatures. Being compared with most of existing methods in the literatures, this method can better deal with gait frequency, local spatial-temporal human pose features, and global body shape statistics. The proposed method is validated on several well known benchmark databases. The extensive and encouraging experimental results show that the proposed algorithm achieves high accuracy, but with low complexity and computational time.