A resource-allocating network for function interpolation
Neural Computation
Robust Real-Time Face Detection
International Journal of Computer Vision
2005 Special issue: Incremental learning of feature space and classifier for face recognition
Neural Networks - 2005 Special issue: IJCNN 2005
Improving AdaBoost Based Face Detection Using Face-Color Preferable Selective Attention
IDEAL '08 Proceedings of the 9th International Conference on Intelligent Data Engineering and Automated Learning
Incremental Learning of Chunk Data for Online Pattern Classification Systems
IEEE Transactions on Neural Networks
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We propose a new approach for a real-time personal authentication system, which consists of a selective face attention model, incremental feature extraction, and an incremental neural classifier model with long-term memory. In this paper, a face-color preferable selective attention combined with the Adaboost algorithm is used to detect human faces, and incremental principal component analysis (IPCA) and resource allocating network with long-term memory (RAN-LTM) are effectively combined to implement real-time personal authentication systems. The biologically motivated face-color preferable selective attention model localizes face candidate regions in a natural scene, and then the Adaboost based face detection process identifies human faces from the localized face-candidate regions. IPCA updates an eigen-space incrementally by rotating eigen-axes and adaptively increasing the eigen-space dimensions. The features extracted by projecting inputs to the eigen-space are given to RAN-LTM which learns facial features incrementally without unexpected forgetting and recognizes faces in real time. The experimental results show that the proposed model successfully recognizes 200 human faces through incremental learning without serious forgetting.