Fast and Robust Face Recognition for Incremental Data

This paper proposes fast and robust face recognition system for incremental data, which come continuously into the system. Fast and robust mean that the face recognition performs rapidly both of training and querying process and steadily recognize face images, which have large lighting variations. The fast training and querying can be performed by implementing compact face features as dimensional reduction of face image and predictive LDA (PDLDA) as face classifier. The PDLDA performs rapidly the features cluster process because the PDLDA does not require to recalculate the between class scatter, Sb, when a new class data is registered into the training data set. In order to get the robust face recognition achievement, we develop the lighting compensation, which works based on neighbor analysis and is integrated to the PDLDA based face recognition.