Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Gait Sequence Analysis Using Frieze Patterns
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part II
Gait-Based Recognition of Humans Using Continuous HMMs
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
The HumanID Gait Challenge Problem: Data Sets, Performance, and Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Gait Verification Using Probabilistic Methods
WACV-MOTION '05 Proceedings of the Seventh IEEE Workshops on Application of Computer Vision (WACV/MOTION'05) - Volume 1 - Volume 01
A Supervised Learning Framework for Generic Object Detection in Images
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Individual Recognition Using Gait Energy Image
IEEE Transactions on Pattern Analysis and Machine Intelligence
Gait recognition using image self-similarity
EURASIP Journal on Applied Signal Processing
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
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
Identification of humans using gait
IEEE Transactions on Image Processing
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Recognizing people by gait has a unique advantage over other biometrics: it has potential for use at a distance when other biometrics might be at too low a resolution, or might be obscured. In this paper, an improved method for gait recognition is proposed. The proposed work introduces a nonlinear machine learning method, kernel Principal Component Analysis (KPCA), to extract gait features from silhouettes for individual recognition. Binarized silhouette of a motion object is first represented by four 1-D signals which are the basic image features called the distance vectors. The distance vectors are differences between the bounding box and silhouette, and extracted using four projections to silhouette. Classic linear feature extraction approaches, such as PCA, LDA, and FLDA, only take the 2-order statistics among gait patterns into account, and are not sensitive to higher order statistics of data. Therefore, KPCA is used to extract higher order relations among gait patterns for future recognition. Fast Fourier Transform (FFT) is employed as a preprocessing step to achieve translation invariant on the gait patterns accumulated from silhouette sequences which are extracted from the subjects walk in different speed and/or different time. The experiments are carried out on the CMU and the USF gait databases and presented based on the different training gait cycles. Finally, the performance of the proposed algorithm is comparatively illustrated to take into consideration the published gait recognition approaches.