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 recognition for human identification based on ICA and fuzzy SVM through multiple views fusion
Pattern Recognition Letters
IEEE Transactions on Pattern Analysis and Machine Intelligence
Human Activity Recognition with Metric Learning
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part I
Distance Metric Learning for Large Margin Nearest Neighbor Classification
The Journal of Machine Learning Research
Uncorrelated discriminant simplex analysis for view-invariant gait signal computing
Pattern Recognition Letters
Human identification from human movements
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Gait-based human age estimation
IEEE Transactions on Information Forensics and Security
Identification of humans using gait
IEEE Transactions on Image Processing
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This paper presents a new activity-based person identification method using sparse coding and discriminative metric learning. Different from gait recognition where human walking activity is only utilized for person identification, we aim to recognize people from different activities such as running, jumping, skipping, and so on. For each activity video clip, we extract the binary human body mask using background substraction. Then, we cluster these body masks into a number of clusters by sparse coding with mean pooling to extract features for each video clip. Subsequently, we learn a discriminative distance metric under which intraclass (activities performed by the same person) variations are minimized and the interclass (activities performed by different persons) are maximized, simultaneously, such that more discriminative information can be exploited for recognition. Experimental results on a publicly available database are presented to show the efficacy of our proposed method.