An Analytic-to-Holistic Approach for Face Recognition Based on a Single Frontal View

We propose an analytic-to-holistic approach which can identify faces at different perspective variations. The database for the test consists of 40 frontal-view faces. The first step is to locate 15 feature points on a face. A head model is proposed, and the rotation of the face can be estimated using geometrical measurements. The positions of the feature points are adjusted so that their corresponding positions for the frontal view are approximated. These feature points are then compared with the feature points of the faces in a database using a similarity transform. In the second step, we set up windows for the eyes, nose, and mouth. These feature windows are compared with those in the database by correlation. Results show that this approach can achieve a similar level of performance from different viewing directions of a face. Under different perspective variations, the overall recognition rates are over 84 percent and 96 percent for the first and the first three likely matched faces, respectively.

[1]  Roberto Brunelli,et al.  Robust estimation of correlation with applications to computer vision , 1995, Pattern Recognit..

[2]  Zi-Quan Hong,et al.  Algebraic feature extraction of image for recognition , 1991, Pattern Recognit..

[3]  Ashok Samal,et al.  Automatic recognition and analysis of human faces and facial expressions: a survey , 1992, Pattern Recognit..

[4]  Hanqi Zhuang,et al.  Corner detection by a cost minimization approach , 1993, Pattern Recognit..

[5]  K. Lam,et al.  Fast greedy algorithm for active contours , 1994 .

[6]  Hong Yan,et al.  An Improved Method for Locating and Extracting the Eye in Human Face Images , 1996, Proceedings of 13th International Conference on Pattern Recognition.

[7]  Helen C. Shen,et al.  Face recognition using perspective invariant features , 1994, Pattern Recognit. Lett..

[8]  C. von der Malsburg,et al.  Distortion invariant object recognition by matching hierarchically labeled graphs , 1989, International 1989 Joint Conference on Neural Networks.

[9]  M. K. Khan,et al.  Machine identification of human faces , 1981, Pattern Recognition.

[10]  Osamu Nakamura,et al.  Identification of human faces based on isodensity maps , 1991, Pattern Recognit..

[11]  Chung-Lin Huang,et al.  Human Face Recognition from A Single Front View , 1992, Int. J. Pattern Recognit. Artif. Intell..

[12]  Hong Yan,et al.  Locating and extracting the eye in human face images , 1996, Pattern Recognit..

[13]  Jun S. Huang,et al.  Human face profile recognition by computer , 1990, Pattern Recognit..

[14]  Yong-Qing Cheng,et al.  A novel feature extraction method for image recognition based on similar discriminant function (SDF) , 1993, Pattern Recognit..

[15]  Hong Yan,et al.  Adaptive deformable model for mouth boundary detection , 1998 .

[16]  Hong Yan,et al.  Facial Feature Location and Extraction for Computerized Human Face Recognition , 1994 .

[17]  Roberto Brunelli,et al.  Face Recognition: Features Versus Templates , 1993, IEEE Trans. Pattern Anal. Mach. Intell..

[18]  L. D. Harmon,et al.  Identification of human face profiles by computer , 1978, Pattern Recognit..

[19]  Hong Yan,et al.  Fast algorithm for locating head boundaries , 1994, J. Electronic Imaging.

[20]  Michael Werman,et al.  Similarity and Affine Invariant Distances Between 2D Point Sets , 1995, IEEE Trans. Pattern Anal. Mach. Intell..

[21]  M. Turk,et al.  Eigenfaces for Recognition , 1991, Journal of Cognitive Neuroscience.

[22]  Joachim M. Buhmann,et al.  Distortion Invariant Object Recognition in the Dynamic Link Architecture , 1993, IEEE Trans. Computers.

[23]  A. Yuille.  Deformable Templates for Face Recognition , 1991, Journal of Cognitive Neuroscience.