Handling concept drifts in incremental learning with support vector machines
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Multicategory Classification by Support Vector Machines
Computational Optimization and Applications - Special issue on computational optimization—a tribute to Olvi Mangasarian, part I
Comprehensive Colour Image Normalization
ECCV '98 Proceedings of the 5th European Conference on Computer Vision-Volume I - Volume I
Incremental Learning with Support Vector Machines
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Human Recognition of Familiar and Unfamiliar People in Naturalistic Video
AMFG '03 Proceedings of the IEEE International Workshop on Analysis and Modeling of Faces and Gestures
Feature fusion of side face and gait for video-based human identification
Pattern Recognition
A review of feature selection techniques in bioinformatics
Bioinformatics
Vision-based production of personalized video
Image Communication
The WEKA data mining software: an update
ACM SIGKDD Explorations Newsletter
Incremental and Decremental Multi-category Classification by Support Vector Machines
ICMLA '09 Proceedings of the 2009 International Conference on Machine Learning and Applications
People re-identification by spectral classification of silhouettes
Signal Processing
IEEE Transactions on Signal Processing
Integrating Face and Gait for Human Recognition at a Distance in Video
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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The goal of this paper is to present a new on-line human recognition system, which is able to classify persons with adaptive abilities using an incremental classifier. The proposed incremental SVM is fast, as its training phase relies on only a few images and it uses the mathematical properties of SVM to update only the needed parts. In our system, first of all, feature extraction and selection are implemented, based on color and texture features (appearance of the person). Then the incremental SVM classifier is introduced to recognize a person from a set of 20 persons in CASIA Gait Database. The proposed incremental classifier is updated step by step when a new frame including a person is presented. With this technique, we achieved a correct classification rate of 98.46%, knowing only 5% of the dataset at the beginning of the experiment. A comparison with a non-incremental technique reaches recognition rate of 99% on the same database. Extended analyses have been carried out and showed that the proposed method can be adapted to on-line setting.