The FERET Evaluation Methodology for Face-Recognition Algorithms
IEEE Transactions on Pattern Analysis and Machine Intelligence
Optimizing search engines using clickthrough data
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
An efficient boosting algorithm for combining preferences
The Journal of Machine Learning Research
The HumanID Gait Challenge Problem: Data Sets, Performance, and Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Individual Recognition Using Gait Energy Image
IEEE Transactions on Pattern Analysis and Machine Intelligence
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 04
General Tensor Discriminant Analysis and Gabor Features for Gait Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fast communication: Gait recognition based on dynamic region analysis
Signal Processing
Detecting Carried Objects in Short Video Sequences
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part III
Biometric Gait Recognition with Carrying and Clothing Variants
PReMI '09 Proceedings of the 3rd International Conference on Pattern Recognition and Machine Intelligence
An efficient gait recognition with backpack removal
EURASIP Journal on Advances in Signal Processing
Efficient algorithms for ranking with SVMs
Information Retrieval
Gait recognition without subject cooperation
Pattern Recognition Letters
Gait recognition using a view transformation model in the frequency domain
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part III
Dyadic transfer learning for cross-domain image classification
ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
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The advantage of gait over other biometrics such as face or fingerprint is that it can operate from a distance and without subject cooperation. However, this also makes gait subject to changes in various covariate conditions including carrying, clothing, surface and view angle. Existing approaches attempt to address these condition changes by feature selection, feature transformation or discriminant subspace learning. However, they suffer from lack of training samples from each subject, can only cope with changes in a subset of conditions with limited success, and are based on the invalid assumption that the covariate conditions are known a priori. They are thus unable to perform gait recognition under a genuine uncooperative setting. We propose a novel approach which casts gait recognition as a bipartite ranking problem and leverages training samples from different classes/people and even from different datasets. This makes our approach suitable for recognition under a genuine uncooperative setting and robust against any covariate types, as demonstrated by our extensive experiments.