Face recognition provides a natural visual interface for human computer interaction(HCI) applications.The process of face recognition,however,is inhibited by variations in the appearance of face images caused by changes in lighting,expression,viewpoint,aging and introduction of occlusion.Although various algorithms have been presented for face recognition,face recognition is still a very challenging topic.A novel approach of real time face recognition for HCI is proposed in the paper.In view of the limits of the popular approaches to foreground segmentation,wavelet multi-scale transform based background subtraction is developed to extract foreground objects.The optimal selection of the threshold is automatically determined,which does not require any complex supervised training or manual experimental calibration.A robust real time face recognition algorithm is presented,which combines the projection matrixes without iteration and kernel Fisher discriminant analysis(KFDA) to overcome some difficulties existing in the real face recognition.Superior performance of the proposed algorithm is demonstrated by comparing with other algorithms through experiments.The proposed algorithm can also be applied to the video image sequences of natural HCI.
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