Much of the work on faces in computer vision has been focused on face recognition or facial expression analysis, but has not been directly related with face direction detection. In this paper, we propose a vision-based approach to detect a face direction from a single monocular view of a face by using a facial feature called facial triangle, which is formed by two eyebrows and the lower lip. Specifically, the proposed method introduces simple formulas to detect face rotation, horizontally and vertically, using the facial triangle. It makes no assumption about the structure of the face and produces an accurate estimate of face direction.

Chen proposed an image quality assessment method to evaluate image quality at a ratio of noise in an image. However, Chen's method had some drawbacks that unnoticeable noise is reflected in the evaluation or noise position is not accurately detected. Therefore, in this paper, we propose a new image quality measurement scheme using the mean-centered WLNI (Weber's Law Noise Identifier) and the saliency map. The experimental results show that the proposed method outperforms Chen's and agrees more consistently with human visual judgment.