An Incremental Learning Algorithm for Face Recognition
ECCV '02 Proceedings of the International ECCV 2002 Workshop Copenhagen on Biometric Authentication
2005 Special issue: Incremental learning of feature space and classifier for face recognition
Neural Networks - 2005 Special issue: IJCNN 2005
Journal on Image and Video Processing - Anthropocentric Video Analysis: Tools and Applications
The AIT Multimodal Person Identification System for CLEAR 2007
Multimodal Technologies for Perception of Humans
Identity Management in Face Recognition Systems
Biometrics and Identity Management
Person recognition using facial video information: A state of the art
Journal of Visual Languages and Computing
Where and Who? Person Tracking and Recognition System
Proceedings of the 2007 conference on Emerging Artificial Intelligence Applications in Computer Engineering: Real Word AI Systems with Applications in eHealth, HCI, Information Retrieval and Pervasive Technologies
Subclass linear discriminant analysis for video-based face recognition
Journal of Visual Communication and Image Representation
Learning to recognize familiar faces in the real world
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
A bounded version of online boosting on open-ended data streams
DaWaK'11 Proceedings of the 13th international conference on Data warehousing and knowledge discovery
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The current technology in computer vision requires humans to collect images, store images, segment images for computers and train computer recognition systems using these images. It is unlikely that such a manual labor process can meet the demands of many challenging recognition tasks. Our goal is to enable machines to learn directly from sensory input streams while interacting with the environment including human teachers. We propose a new technique which incrementally derives discriminating features in the input space. Virtual labels are formed by clustering in the output space to extract discriminating features in the input space. We organize the resulting discriminating subspace in a coarse-to-fine fashion and store the information in a decision tree. Such an incremental hierarchical discriminating regression (IHDR) decision tree can be modeled by a hierarchical probability distribution model. We demonstrate the performance of the algorithm on the problem of face recognition using video sequences of 33,889 frames in length from 143 different subjects. A correct recognition rate of 95.1% has been achieved.