Exploring face recognition by combining 3D profiles and contours

Most of the face recognition research performed in the past used 2D intensity images obtained by a photographic camera as the data format for processing, but the algorithms developed based on 2D images are not robust to changes of the conditions in which the images are taken, like the illumination of the environment and the orientation of the subject. With the development of 3D imaging techniques, 3D face recognition is becoming a natural choice to overcome the shortcomings of 2D face recognition, since a 3D face image records the exact geometry of the subject, invariant to illumination and the orientation changes. In this paper, a new algorithm for automatic face recognition, based on the characterization of faces by their contours and profiles, is proposed. Experiments show that the central vertical profile and the contour are both very useful features for face recognition. When combined, better recognition rates can be obtained than just using any of them alone. The performance of the algorithm is also compared with that of the traditional principal component analysis method using a database of 80 subjects. Results show that our method, which characterizes a face through its central vertical profile and contour, can achieve better results and requires less computational power in processing this test database.