Three-dimensional view-invariant face recognition using a hierarchical pose-normalization strategy

Face recognition from three-dimensional (3D) shape data has been proposed as a method of biometric identification as a way of either supplanting or reinforcing a two-dimensional approach. This paper presents a 3D face recognition system capable of recognizing the identity of an individual from a 3D facial scan in any pose across the view-sphere, by suitably comparing it with a set of models (all in frontal pose) stored in a database. The system makes use of only 3D shape data, ignoring textural information completely. Firstly, we propose a generic learning strategy using support vector regression [Burges, Data Mining Knowl Discov 2(2): 121–167, 1998] to estimate the approximate pose of a 3D head. The support vector machine (SVM) is trained on range images in several poses belonging to only a small set of individuals and is able to coarsely estimate the pose of any unseen facial scan. Secondly, we propose a hierarchical two-step strategy to normalize a facial scan to a nearly frontal pose before performing any recognition. The first step consists of either a coarse normalization making use of facial features or the generic learning algorithm using the SVM. This is followed by an iterative technique to refine the alignment to the frontal pose, which is basically an improved form of the Iterated Closest Point Algorithm [Besl and Mckay, IEEE Trans Pattern Anal Mach Intell 14(2):239–256, 1992]. The latter step produces a residual error value, which can be used as a metric to gauge the similarity between two faces. Our two-step approach is experimentally shown to outperform both of the individual normalization methods in terms of recognition rates, over a very wide range of facial poses. Our strategy has been tested on a large database of 3D facial scans in which the training and test images of each individual were acquired at significantly different times, unlike all except two of the existing 3D face recognition methods.

[1]  Andrew W. Fitzgibbon.  Robust registration of 2D and 3D point sets , 2003, Image Vis. Comput..

[2]  Thomas Vetter,et al.  A morphable model for the synthesis of 3D faces , 1999, SIGGRAPH.

[3]  Zhengyou Zhang.  On Local Matching of Free-form Curves , 1992, BMVC.

[4]  Heinz Hügli,et al.  A multi-resolution scheme ICP algorithm for fast shape registration , 2002, Proceedings. First International Symposium on 3D Data Processing Visualization and Transmission.

[5]  Jon Louis Bentley,et al.  K-d trees for semidynamic point sets , 1990, SCG '90.

[6]  Bernhard Schölkopf,et al.  A tutorial on support vector regression , 2004, Stat. Comput..

[7]  Gérard G. Medioni,et al.  Object modeling by registration of multiple range images , 1991, Proceedings. 1991 IEEE International Conference on Robotics and Automation.

[8]  Katsuhiko Sakaue,et al.  Registration and integration of multiple range images for 3-D model construction , 1996, Proceedings of 13th International Conference on Pattern Recognition.

[9]  Nicholas I. Fisher,et al.  Correlation coefficients for random variables on a unit sphere or hypersphere , 1986 .

[10]  Gaile G. Gordon,et al.  Face recognition based on depth maps and surface curvature , 1991, Optics & Photonics.

[11]  Vladimir Vapnik,et al.  Statistical learning theory , 1998 .

[12]  Ernest L. Hall,et al.  Three-Dimensional Moment Invariants , 1980, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[13]  Chin Seng Chua,et al.  Point Signatures: A New Representation for 3D Object Recognition , 1997, International Journal of Computer Vision.

[14]  Shaogang Gong,et al.  Face distributions in similarity space under varying head pose , 2001, Image Vis. Comput..

[15]  William H. Press,et al.  The Art of Scientific Computing Second Edition , 1998 .

[16]  Anand Rangarajan,et al.  A new algorithm for non-rigid point matching , 2000, Proceedings IEEE Conference on Computer Vision and Pattern Recognition. CVPR 2000 (Cat. No.PR00662).

[17]  Michael G. Strintzis,et al.  Use of depth and colour eigenfaces for face recognition , 2003, Pattern Recognit. Lett..

[18]  William H. Press,et al.  Book-Review - Numerical Recipes in Pascal - the Art of Scientific Computing , 1989 .

[19]  Kristin P. Bennett,et al.  Support vector machines: hype or hallelujah? , 2000, SKDD.

[20]  Qiang Ji,et al.  3D face pose discrimination using wavelets , 2001, Proceedings 2001 International Conference on Image Processing (Cat. No.01CH37205).

[21]  Ajit Rajwade,et al.  Facial pose from 3D data , 2006, Image Vis. Comput..

[22]  Paul J. Besl,et al.  A Method for Registration of 3-D Shapes , 1992, IEEE Trans. Pattern Anal. Mach. Intell..

[23]  Anil K. Jain,et al.  Three-dimensional model based face recognition , 2004, ICPR 2004.

[24]  José F. Vélez,et al.  Face recognition using 3D surface extracted descriptors , 2003 .

[25]  Jim Austin,et al.  Three-Dimensional Face Recognition: A Fishersurface Approach , 2004, ICIAR.

[26]  Richard O. Duda,et al.  Pattern classification and scene analysis , 1974, A Wiley-Interscience publication.

[27]  Shaogang Gong,et al.  Support vector regression and classification based multi-view face detection and recognition , 2000, Proceedings Fourth IEEE International Conference on Automatic Face and Gesture Recognition (Cat. No. PR00580).

[28]  Nicholas Ayache,et al.  Rigid, affine and locally affine registration of free-form surfaces , 1996, International Journal of Computer Vision.

[29]  Chin-Seng Chua,et al.  Face recognition from 2D and 3D images using 3D Gabor filters , 2005, Image Vis. Comput..

[30]  Franco P. Preparata,et al.  Computational Geometry , 1985, Texts and Monographs in Computer Science.

[31]  John Daugman,et al.  Face and Gesture Recognition: Overview , 1997, IEEE Trans. Pattern Anal. Mach. Intell..

[32]  Hyeonjoon Moon,et al.  The FERET Evaluation Methodology for Face-Recognition Algorithms , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[33]  Anil K. Jain,et al.  Matching 2.5D Scans for Face Recognition , 2004, ICBA.

[34]  Marti A. Hearst.  Trends & Controversies: Support Vector Machines , 1998, IEEE Intell. Syst..

[35]  Robert P. W. Duin,et al.  Support vector domain description , 1999, Pattern Recognit. Lett..

[36]  Bernhard Schölkopf,et al.  Kernel machine based learning for multi-view face detection and pose estimation , 2001, Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001.

[37]  Chin-Seng Chua,et al.  Facial feature detection and face recognition from 2D and 3D images , 2002, Pattern Recognit. Lett..

[38]  Sunghee Choi,et al.  The power crust , 2001, SMA '01.

[39]  Patrick J. Flynn,et al.  Face Recognition Using 2D and 3D Facial Data , 2003 .

[40]  Hiromi T. Tanaka,et al.  Curvature-based face surface recognition using spherical correlation-principal directions for curved object recognition , 1996, Proceedings of 13th International Conference on Pattern Recognition.

[41]  H. Sebastian Seung,et al.  Learning the parts of objects by non-negative matrix factorization , 1999, Nature.

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

[43]  Feng Han,et al.  3D human face recognition using point signature , 2000, Proceedings Fourth IEEE International Conference on Automatic Face and Gesture Recognition (Cat. No. PR00580).

[44]  Evangelos E. Milios,et al.  Matching range images of human faces , 1990, [1990] Proceedings Third International Conference on Computer Vision.

[45]  Michael Ian Shamos,et al.  Computational geometry: an introduction , 1985 .

[46]  Horst Bunke,et al.  Face recognition using range images , 1997, Proceedings. International Conference on Virtual Systems and MultiMedia VSMM '97 (Cat. No.97TB100182).

[47]  F. A. Seiler,et al.  Numerical Recipes in C: The Art of Scientific Computing , 1989 .