A novel human-computer interaction(HCI)is developed based on multimodal visual features aiming at some limits at present.Two-dimensional Gabor wavelet is adopted to extract some visual features of global face orientation

which overcomes some difficulties including extraction of some facial distinct features

discrimination among some different facial orientations.An efficient and fast approach to locating center of eyes is proposed based on facial geometric distributions without considering facial resolution

eyes closing or opening and user's wearing.Some prominent multimodal visual features for classification are selected to machine learning and training to determine the pointing target after evaluating performance of some extracted visual features.Non-wearable and natural HCI modal can be realized in which user can move freely without wearing any markers when he points at some targets.Their daily skills can be exerted fully during HCI.Experiment results indicate that the developed approach is efficient and can be used to natural non-wearable HCI.