The FERET Evaluation Methodology for Face-Recognition Algorithms
IEEE Transactions on Pattern Analysis and Machine Intelligence
Gait Sequence Analysis Using Frieze Patterns
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part II
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
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
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
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 novel approach on silhouette based human motion analysis for gait recognition
ISVC'05 Proceedings of the First international conference on Advances in Visual Computing
Identification of humans using gait
IEEE Transactions on Image Processing
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Human identification at a distance by analysis of gait patterns extracted from video has recently become very popular research in biometrics. This paper presents multi-projections based approach to extract gait patterns for human recognition. Binarized silhouette of a motion object is represented by 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. Based on normalized correlation on the distance vectors, gait cycle estimation is first performed to extract the gait cycle. Second, eigenspace transformation is applied to time-varying distance vectors and the statistical distance based supervised pattern classification is then performed in the lower-dimensional eigenspace for human identification. A fusion strategy developed is finally executed to produce final decision. Experimental results on four databases demonstrate that the right person in top two matches 100% of the times for the cases where training and testing sets corresponds to the same walking styles, and in top three-four matches 100% of the times for training and testing sets corresponds to the different walking styles.