Vision interaction is one of important aspects of human-computer interaction

and the facial feature extraction is crucial to vision interaction. This paper applies the curvelet transform to the face processing to extract facial feature more effectively. It overcomes the weakness of the wavelet transform which is unable to efficiently extract curve features of face images. An optimized method based on Adaboost and curvelet transform is proposed for face detection. A new approach combining SVM and curvelet transform is designed for facial expression recognition. Experiments on face detection

face recognition and facial expression recognition are carried out. The results reveal that curvelet transform has distinct advantages in facial feature extraction

and lays a good foundation for the further work of the natural human computer interaction.