Approach of human face recognition based on SIFT feature extraction and 3D rotation model

One of the main problems in face recognition is the influences of varying poses and illumination. This paper proposes a novel method of human face recognition to overcome the influences. The method is mainly based on the SIFT feature extraction and 3D rotation model of heads. SIFT descriptor is used to select key points of faces in the database including seventy people with nine poses in the first stage. Then according to the feature of a test face, matching algorithm is applied to find its candidates from the database and defines some criteria to convince the final matching result in the second stage. If satisfactory results can not be gained in the second stage, the 3D rotation method will be triggered and it makes a secondary decision by normalizing the depth information of the faces. This algorithm is tested in the face database and the result shows that the accuracy is as high as 94.45%.