2D and 3D face recognition: A survey

Government agencies are investing a considerable amount of resources into improving security systems as result of recent terrorist events that dangerously exposed flaws and weaknesses in today's safety mechanisms. Badge or password-based authentication procedures are too easy to hack. Biometrics represents a valid alternative but they suffer of drawbacks as well. Iris scanning, for example, is very reliable but too intrusive; fingerprints are socially accepted, but not applicable to non-consentient people. On the other hand, face recognition represents a good compromise between what's socially acceptable and what's reliable, even when operating under controlled conditions. In last decade, many algorithms based on linear/nonlinear methods, neural networks, wavelets, etc. have been proposed. Nevertheless, Face Recognition Vendor Test 2002 shown that most of these approaches encountered problems in outdoor conditions. This lowered their reliability compared to state of the art biometrics. This paper provides an ''ex cursus'' of recent face recognition research trends in 2D imagery and 3D model based algorithms. To simplify comparisons across different approaches, tables containing different collection of parameters (such as input size, recognition rate, number of addressed problems) are provided. This paper concludes by proposing possible future directions.

[1]  Anil K. Jain,et al.  Three-dimensional model based face recognition , 2004, Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004..

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

[3]  Ja-Chen Lin,et al.  A new LDA-based face recognition system which can solve the small sample size problem , 1998, Pattern Recognit..

[4]  Andreas Lanitis,et al.  Robust face recognition using automatic age normalization , 2000, 2000 10th Mediterranean Electrotechnical Conference. Information Technology and Electrotechnology for the Mediterranean Countries. Proceedings. MeleCon 2000 (Cat. No.00CH37099).

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

[6]  Shigeo Abe DrEng.  Pattern Classification , 2001, Springer London.

[7]  Chengjun Liu,et al.  Gabor-based kernel PCA with fractional power polynomial models for face recognition , 2004, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[8]  Berk Gökberk,et al.  3D shape-based face recognition using automatically registered facial surfaces , 2004, Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004..

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

[10]  Hua Yu,et al.  A direct LDA algorithm for high-dimensional data - with application to face recognition , 2001, Pattern Recognit..

[11]  Konstantinos N. Plataniotis,et al.  Face recognition using LDA-based algorithms , 2003, IEEE Trans. Neural Networks.

[12]  J FlynnPatrick,et al.  An Evaluation of Multimodal 2D+3D Face Biometrics , 2005 .

[13]  Hiromi T. Tanaka,et al.  Curvature-based face surface recognition using spherical correlation. Principal directions for curved object recognition , 1998, Proceedings Third IEEE International Conference on Automatic Face and Gesture Recognition.

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

[15]  Behrooz Kamgar-Parsi,et al.  Face Recognition with 3D Model-Based Synthesis , 2004, ICBA.

[16]  Sun-Yuan Kung,et al.  Face recognition/detection by probabilistic decision-based neural network , 1997, IEEE Trans. Neural Networks.

[17]  Sridha Sridharan,et al.  Face recognition using fractal codes , 2001, Proceedings 2001 International Conference on Image Processing (Cat. No.01CH37205).

[18]  Michele Nappi,et al.  Deferring range/domain comparisons in fractal image compression , 2003, 12th International Conference on Image Analysis and Processing, 2003.Proceedings..

[19]  Alexander M. Bronstein,et al.  Expression-Invariant 3D Face Recognition , 2003, AVBPA.

[20]  Thomas S. Huang,et al.  Image processing , 1971 .

[21]  Aleix M. Martínez,et al.  Recognizing Imprecisely Localized, Partially Occluded, and Expression Variant Faces from a Single Sample per Class , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[22]  Shimon Ullman,et al.  Face Recognition: The Problem of Compensating for Changes in Illumination Direction , 1997, IEEE Trans. Pattern Anal. Mach. Intell..

[23]  Hong Yan,et al.  Face recognition by fractal transformations , 1999, 1999 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings. ICASSP99 (Cat. No.99CH36258).

[24]  Andrea Salgian,et al.  Thermal face recognition over time , 2004, Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004..

[25]  Margaret L. Johnson.  Biometrics and the threat to civil liberties , 2004, Computer.

[26]  Karl Sammut,et al.  Fractal face representation and recognition , 1997, 1997 IEEE International Conference on Systems, Man, and Cybernetics. Computational Cybernetics and Simulation.

[27]  Hakan Cevikalp,et al.  Discriminative common vectors for face recognition , 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[28]  Meng Joo Er,et al.  Face recognition with radial basis function (RBF) neural networks , 2002, IEEE Trans. Neural Networks.

[29]  Marc Acheroy,et al.  Automatic 3D face authentication , 2000, Image Vis. Comput..

[30]  Christoph von der Malsburg,et al.  Pose-invariant face recognition with parametric linear subspaces , 2002, Proceedings of Fifth IEEE International Conference on Automatic Face Gesture Recognition.

[31]  P J. Phillips,et al.  Face Recognition Vendor Test 2000: Evaluation Report , 2001 .

[32]  Andrea Salgian,et al.  Thermal face recognition in an operational scenario , 2004, Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004. CVPR 2004..

[33]  Georgios Tziritas,et al.  Wavelet packet analysis for face recognition , 2000, Image Vis. Comput..

[34]  Norbert Krüger,et al.  Face recognition by elastic bunch graph matching , 1997, Proceedings of International Conference on Image Processing.

[35]  Timothy F. Cootes,et al.  Toward Automatic Simulation of Aging Effects on Face Images , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[36]  Daniel Rueckert,et al.  Evaluation of automatic 4D face recognition using surface and texture registration , 2004, Sixth IEEE International Conference on Automatic Face and Gesture Recognition, 2004. Proceedings..

[37]  Josef Kittler,et al.  Independent component analysis in a facial local residue space , 2003, 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings..

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

[39]  Ioannis A. Kakadiaris,et al.  Face Recognition in the Thermal Infrared Spectrum , 2004, 2004 Conference on Computer Vision and Pattern Recognition Workshop.

[40]  Ralph Gross,et al.  Eigen light-fields and face recognition across pose , 2002, Proceedings of Fifth IEEE International Conference on Automatic Face Gesture Recognition.

[41]  Jieping Ye,et al.  Linear projection methods in face recognition under unconstrained illuminations: a comparative study , 2004, Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004. CVPR 2004..

[42]  Hyeonjoon Moon,et al.  The FERET evaluation methodology for face-recognition algorithms , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[43]  David J. Kriegman,et al.  Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection , 1996, ECCV.

[44]  Xin Chen,et al.  PCA-Based Face Recognition in Infrared Imagery: Baseline and Comparative Studies , 2003, AMFG.

[45]  Tieniu Tan,et al.  Depth vs. intensity: which is more important for face recognition? , 2004, Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004..

[46]  Yuxiao Hu,et al.  Automatic 3D reconstruction for face recognition , 2004, Sixth IEEE International Conference on Automatic Face and Gesture Recognition, 2004. Proceedings..

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

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

[49]  Lawrence Sirovich,et al.  Application of the Karhunen-Loeve Procedure for the Characterization of Human Faces , 1990, IEEE Trans. Pattern Anal. Mach. Intell..

[50]  David G. Stork,et al.  Pattern Classification , 1973 .

[51]  Avinash C. Kak,et al.  PCA versus LDA , 2001, IEEE Trans. Pattern Anal. Mach. Intell..

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

[53]  Florent Perronnin,et al.  An introduction to biometrics and face recognition , 2003 .

[54]  A-Nasser Ansari,et al.  3D face modeling using two views and a generic face model with application to 3D face recognition , 2003, Proceedings of the IEEE Conference on Advanced Video and Signal Based Surveillance, 2003..

[55]  Jamie Sherrah,et al.  False alarm rate: a critical performance measure for face recognition , 2004, Sixth IEEE International Conference on Automatic Face and Gesture Recognition, 2004. Proceedings..

[56]  Takio Kurita,et al.  Recognition and detection of occluded faces by a neural network classifier with recursive data reconstruction , 2003, Proceedings of the IEEE Conference on Advanced Video and Signal Based Surveillance, 2003..

[57]  Hichem Sahbi,et al.  Robust Face Recognition Using Dynamic Space Warping , 2002, Biometric Authentication.

[58]  Patrick J. Flynn,et al.  An evaluation of multimodal 2D+3D face biometrics , 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[59]  Jim Austin,et al.  Three-dimensional face recognition: an eigensurface approach , 2004, 2004 International Conference on Image Processing, 2004. ICIP '04..

[60]  Thomas Vetter,et al.  Face Recognition Based on Fitting a 3D Morphable Model , 2003, IEEE Trans. Pattern Anal. Mach. Intell..

[61]  S. Sridharan,et al.  Face recognition from 3D data using Iterative Closest Point algorithm and Gaussian mixture models , 2004, Proceedings. 2nd International Symposium on 3D Data Processing, Visualization and Transmission, 2004. 3DPVT 2004..

[62]  Terence Sim,et al.  The CMU Pose, Illumination, and Expression Database , 2003, IEEE Trans. Pattern Anal. Mach. Intell..

[63]  Luc Cournoyer,et al.  The NRCC three-dimensional image data files , 1988 .

[64]  David J. Kriegman,et al.  From Few to Many: Illumination Cone Models for Face Recognition under Variable Lighting and Pose , 2001, IEEE Trans. Pattern Anal. Mach. Intell..

[65]  Bruce J. Tromberg,et al.  Face Recognition in Hyperspectral Images , 2003, IEEE Trans. Pattern Anal. Mach. Intell..

[66]  Zhaohui Wu,et al.  Sphere-Spin-Image: A Viewpoint-Invariant Surface Representation for 3D Face Recognition , 2004, International Conference on Computational Science.

[67]  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).

[68]  Marian Stewart Bartlett,et al.  Face recognition by independent component analysis , 2002, IEEE Trans. Neural Networks.