An Efficient 3D Geometrical Consistency Criterion for Detection of a Set of Facial Feature Points

We propose a novel efficient three-dimensional geometrical consistency criterion for detection of a set of facial feature points. Many face recognition methods employing a single image require localization of particular facial feature points and their performance is highly dependent on localization accuracy in detecting these feature points. The proposed method is able to calculate alignment error of a point set rapidly because calculation is not iterative. Also the method does not depend on the type of point detection method used and no learning is needed. Independently detected point sets are evaluated through matching to a three-dimensional generic face model. Correspondence error is defined by the distance between the feature points defined in the model and those detected. The proposed criterion is evaluated through experiment using various facial feature point sets on face images.