State-of-the-art of 3D facial reconstruction methods for face recognition based on a single 2D training image per person
3D facial reconstruction systems attempt to reconstruct 3D facial models of individuals from their 2D photographic images or video sequences. Currently published face recognition systems, which exhibit well-known deficiencies, are largely based on 2D facial images, although 3D image capture systems...
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| Published in: | Pattern recognition letters Vol. 30; no. 10; pp. 908 - 913 |
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| Main Authors: | , |
| Format: | Journal Article |
| Language: | English |
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Elsevier B.V
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| ISSN: | 0167-8655, 1872-7344 |
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| Abstract | 3D facial reconstruction systems attempt to reconstruct 3D facial models of individuals from their
2D photographic images or video sequences. Currently published face recognition systems, which exhibit well-known deficiencies, are largely based on 2D facial images, although 3D image capture systems can better encapsulate the 3D geometry of the human face. Accordingly, face recognition research is gradually shifting from the legacy 2D domain to the more sophisticated 2D to 3D or 2D/3D hybrid domain. Currently there exist four methods for 3D facial reconstruction. These are: Stochastic Newton Optimization method (SNO) [Blanz, V., Vetter, T., 1999. A morphable model for the synthesis of 3D faces. In: Proc. 26th Annu. Conf. on Computer Graphics and Interactive Techniques, SIGGRAPH. pp. 187–194; Blanz, V., Vetter, T., 2003. Face recognition based on fitting a 3D morphable model. IEEE Trans. Pattern Anal. Machine Intell. 25(9), 1063–1074; Blanz, V., 2001. Automatische Rekonstruction der Dreidimensionalen Form von Gesichtern aus einem Einzelbild. Ph.D. Thesis, Universitat Tubingen, Germany] inverse compositional image alignment algorithm (ICIA) [Romdhani, S., Vetter, T., 2003. Efficient, robust and accurate fitting of a 3D morphable model. In: IEEE Int. Conf. on Computer Vision, vol. 2, no. 1. pp. 59–66], linear shape and texture fitting algorithm (LiST) [Romdhani, S., Blanz, V., Vetter, T., 2002. Face identification by fitting a 3D morphable model using linear shape and texture error functions. In: Proc. ECCV, vol. 4. pp. 3–19], and shape alignment and interpolation method correction (SAIMC) [Jiang, D., Hu, Y., Yan, S., Zhang, L., Zhang, H., Gao, W., 2005. Efficient 3D reconstruction for face recognition. Pattern Recogn. 38(6), 787–798]. The first three, SNO, ICIA
+
3DMM, and LiST can be classified as “analysis-by-synthesis” techniques and SAIMC can be separately classified as a “3D supported 2D model”. In this paper, we introduce, discuss and analyze the difference between these two frameworks. We begin by presenting the 3D morphable model (3DMM; Blanz and Vetter, 1999), which forms the foundation of all four of the reconstruction techniques described here. This is followed by a review of the basic “analysis-by-synthesis” framework and a comparison of the three methods that employ this approach. We next review the “3D supported 2D model” framework and introduce the SAIMC method, comparing it to the other three. The characteristics of all four methods are summarized in a table that should facilitate further research on this topic. |
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| AbstractList | 3D facial reconstruction systems attempt to reconstruct 3D facial models of individuals from their
2D photographic images or video sequences. Currently published face recognition systems, which exhibit well-known deficiencies, are largely based on 2D facial images, although 3D image capture systems can better encapsulate the 3D geometry of the human face. Accordingly, face recognition research is gradually shifting from the legacy 2D domain to the more sophisticated 2D to 3D or 2D/3D hybrid domain. Currently there exist four methods for 3D facial reconstruction. These are: Stochastic Newton Optimization method (SNO) [Blanz, V., Vetter, T., 1999. A morphable model for the synthesis of 3D faces. In: Proc. 26th Annu. Conf. on Computer Graphics and Interactive Techniques, SIGGRAPH. pp. 187–194; Blanz, V., Vetter, T., 2003. Face recognition based on fitting a 3D morphable model. IEEE Trans. Pattern Anal. Machine Intell. 25(9), 1063–1074; Blanz, V., 2001. Automatische Rekonstruction der Dreidimensionalen Form von Gesichtern aus einem Einzelbild. Ph.D. Thesis, Universitat Tubingen, Germany] inverse compositional image alignment algorithm (ICIA) [Romdhani, S., Vetter, T., 2003. Efficient, robust and accurate fitting of a 3D morphable model. In: IEEE Int. Conf. on Computer Vision, vol. 2, no. 1. pp. 59–66], linear shape and texture fitting algorithm (LiST) [Romdhani, S., Blanz, V., Vetter, T., 2002. Face identification by fitting a 3D morphable model using linear shape and texture error functions. In: Proc. ECCV, vol. 4. pp. 3–19], and shape alignment and interpolation method correction (SAIMC) [Jiang, D., Hu, Y., Yan, S., Zhang, L., Zhang, H., Gao, W., 2005. Efficient 3D reconstruction for face recognition. Pattern Recogn. 38(6), 787–798]. The first three, SNO, ICIA
+
3DMM, and LiST can be classified as “analysis-by-synthesis” techniques and SAIMC can be separately classified as a “3D supported 2D model”. In this paper, we introduce, discuss and analyze the difference between these two frameworks. We begin by presenting the 3D morphable model (3DMM; Blanz and Vetter, 1999), which forms the foundation of all four of the reconstruction techniques described here. This is followed by a review of the basic “analysis-by-synthesis” framework and a comparison of the three methods that employ this approach. We next review the “3D supported 2D model” framework and introduce the SAIMC method, comparing it to the other three. The characteristics of all four methods are summarized in a table that should facilitate further research on this topic. |
| Author | (Chris) Yu, Yingfeng Levine, Martin D. |
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| Cites_doi | 10.1007/3-540-49437-5_10 10.1016/j.patcog.2004.11.004 10.1109/TPAMI.1984.4767521 10.1109/ICCV.2003.1238314 10.1016/j.cviu.2005.05.005 10.1109/TPAMI.2003.1227983 10.1145/344779.344951 10.1007/3-540-47979-1_1 10.1145/311535.311556 |
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| Keywords | 3D morphable model 3D reconstruction Face recognition Single 2D training image State-of-the-art Biometrics State of the art Photographic image Image processing Video signal state-of-the-art Stochastic method Volume reconstruction Texture Learning Image matching Image sequence Computer graphics Newton method Computer vision Sequence alignment Error function Pattern recognition Algorithm Interactive system Photography Three dimensional model Tridimensional image Automatic recognition |
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| References_xml | – reference: Blanz, V., Vetter, T., 1999. A morphable model for the synthesis of 3D faces. In: Proc. 26th Annu. Conf. on Computer Graphics and Interactive Techniques. SIGGRAPH. pp. 187–194. – volume: 25 start-page: 1063 year: 2003 end-page: 1074 ident: bib6 article-title: Face recognition based on fitting a 3D morphable model publication-title: IEEE Trans. Pattern Anal. Machine Intell. – reference: Bergen, J.R., Hingorani, R., 1990, Hierarchical motion-based frame rate conversion. Technical Report. David Sarnoff Research Center, Princeton, NJ. – reference: Blanz, V., 2001. Automatische Rekonstruction der Dreidimensionalen Form von Gesichtern aus einem Einzelbild. Ph.D. Thesis, Universitat Tubingen, Germany. – reference: Matusik, W., Buehler, C., Raskar, R., Gortler, S.J., McMillan, L., 2000. Image-based visual hulls. In: Proc. Int. Conf. on Computer Graphics and Interactive Techniques, SIGGRAPH, 2000. pp. 369–374. – reference: Bregler, C., Hertzmann, A., Biermann, H., 2000. Recovering non-rigid 3D shape from image streams. In: Proc. IEEE Comput. Soc. Conf. on Computer Vision and Pattern Recognition, vol. 2. pp. 690–696. – volume: 38 start-page: 787 year: 2005 end-page: 798 ident: bib13 article-title: Efficient 3D reconstruction for face recognition publication-title: Pattern Recogn. – reference: Gill, G.S., Levine, M.D., 2005. Searching for the holy grail: A completely automated 3D morphable model. Technical Report, March 15, 2005. Department of Electrical & Computer Engineering & Center for Intelligent Machines, McGill University Montreal, Canada. – start-page: 768 year: 2005 ident: bib20 article-title: 3D morphable face model, a unified approach for analysis and synthesis of image publication-title: Face Processing: Advanced Modeling and Methods – reference: Baker, S., Gross, R., Matthews, I., 2002. Lucas–Kanade 20 years on: A unifying framework: Part 1. Technical Report CMU-RI-TR-02-16. Robotics Institute, Carnegie Mellon University. – reference: Hu, Y., Jiang, D., Yan, S., Zhang, L., Zhang, H., 2004. Automatic 3D reconstruction for face recognition. In: Proc. 6th IEEE Int. Conf. on Automatic Face and Gesture Recognition. pp. 843–848. – reference: Romdhani, S., Blanz, V., Vetter, T., 2002. Face identification by fitting a 3D morphable model using linear shape and texture error functions. In: Proc. ECCV, vol. 4. pp. 3–19. – year: 2004 ident: bib19 article-title: Morphable Models of Faces publication-title: Handbook of Face Recognition – reference: Baker, S., Matthews, I., 2001. Equivalence and efficiency of image alignment algorithms. In: IEEE Comput. Soc. Conf. on Computer Vision and Pattern Recognition (CVPR’01), vol. 1. pp. 1090–1097. – year: 2005 ident: bib23 publication-title: Beyond a single still image: Face recognition from multiple still images and videos – volume: 101 start-page: 1 year: 2004 end-page: 15 ident: bib7 article-title: A survey of approaches and challenges in 3D and multi-modal 3D publication-title: Comput. Vision Image Und. – reference: Zhao, W.Y., Chellappa, R., 2000. SFS based view synthesis for robust face recognition. In: Proc. IEEE Int. Automatic Face and Gesture Recognition. pp. 285–292. – volume: 6 start-page: 288 year: 1983 end-page: 301 ident: bib8 article-title: An extremum principle for shape from contour publication-title: IEEE Trans. Pattern Anal. Machine Intell. – year: 2004 ident: bib11 article-title: Contour-based 3D face modeling from a monocular video publication-title: British Machine Vision Conference, BMVC04, September 7–9 – reference: Moghaddam, B., Lee, J.H., Pfister, H., Machiraju, R., 2003. Model-based 3D face capture with shape-from-silhouettes. In: IEEE Int. Workshop on Analysis and Modeling of Faces and Gestures (AMFG), Bice, France. pp. 20–27. – year: 2005 ident: bib21 article-title: A survey of 3D face recognition methods publication-title: Fifth Int. Conf. on Audio- and Video-Based Biometric Person Authentication – reference: Pollefeys, M., 1999. Metric 3D Surface Reconstruction from Uncalibrated Image Sequences. Ph.D. Thesis, Katholieke Universiteit Leuven. – reference: . – reference: Romdhani, S., Vetter, T., 2003. Efficient, robust and accurate fitting of a 3D morphable model. In: IEEE Int. Conf. on Computer Vision, vol. 2, no. 1. pp. 59–66. – year: 2004 ident: 10.1016/j.patrec.2009.03.011_bib19 article-title: Morphable Models of Faces – ident: 10.1016/j.patrec.2009.03.011_bib16 doi: 10.1007/3-540-49437-5_10 – volume: 38 start-page: 787 issue: 6 year: 2005 ident: 10.1016/j.patrec.2009.03.011_bib13 article-title: Efficient 3D reconstruction for face recognition publication-title: Pattern Recogn. doi: 10.1016/j.patcog.2004.11.004 – year: 2005 ident: 10.1016/j.patrec.2009.03.011_bib21 article-title: A survey of 3D face recognition methods – ident: 10.1016/j.patrec.2009.03.011_bib22 – ident: 10.1016/j.patrec.2009.03.011_bib9 – year: 2005 ident: 10.1016/j.patrec.2009.03.011_bib23 – ident: 10.1016/j.patrec.2009.03.011_bib1 – ident: 10.1016/j.patrec.2009.03.011_bib2 – ident: 10.1016/j.patrec.2009.03.011_bib3 – ident: 10.1016/j.patrec.2009.03.011_bib4 – volume: 6 start-page: 288 issue: 3 year: 1983 ident: 10.1016/j.patrec.2009.03.011_bib8 article-title: An extremum principle for shape from contour publication-title: IEEE Trans. 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Machine Intell. doi: 10.1109/TPAMI.2003.1227983 – ident: 10.1016/j.patrec.2009.03.011_bib14 doi: 10.1145/344779.344951 – ident: 10.1016/j.patrec.2009.03.011_bib15 – start-page: 768 year: 2005 ident: 10.1016/j.patrec.2009.03.011_bib20 article-title: 3D morphable face model, a unified approach for analysis and synthesis of image – ident: 10.1016/j.patrec.2009.03.011_bib18 doi: 10.1007/3-540-47979-1_1 – ident: 10.1016/j.patrec.2009.03.011_bib5 doi: 10.1145/311535.311556 |
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| SubjectTerms | 3D morphable model 3D reconstruction Applied sciences Artificial intelligence Computer science; control theory; systems Exact sciences and technology Face recognition Image processing Information, signal and communications theory Pattern recognition Pattern recognition. Digital image processing. Computational geometry Signal processing Single 2D training image State-of-the-art Telecommunications and information theory |
| Title | State-of-the-art of 3D facial reconstruction methods for face recognition based on a single 2D training image per person |
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