A morphable model for the synthesis of 3D faces
Proceedings of the 26th annual conference on Computer graphics and interactive techniques
Proceedings of the 27th annual conference on Computer graphics and interactive techniques
Face Identification by Fitting a 3D Morphable Model Using Linear Shape and Texture Error Functions
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part IV
SFS Based View Synthesis for Robust Face Recognition
FG '00 Proceedings of the Fourth IEEE International Conference on Automatic Face and Gesture Recognition 2000
Face Recognition Based on Fitting a 3D Morphable Model
IEEE Transactions on Pattern Analysis and Machine Intelligence
Model-Based 3D Face Capture with Shape-from-Silhouettes
AMFG '03 Proceedings of the IEEE International Workshop on Analysis and Modeling of Faces and Gestures
Efficient, Robust and Accurate Fitting of a 3D Morphable Model
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Face Processing: Advanced Modeling and Methods
Face Processing: Advanced Modeling and Methods
A survey of approaches and challenges in 3D and multi-modal 3D + 2D face recognition
Computer Vision and Image Understanding
Efficient 3D reconstruction for face recognition
Pattern Recognition
Automatic 3D reconstruction for face recognition
FGR' 04 Proceedings of the Sixth IEEE international conference on Automatic face and gesture recognition
An Extremum Principle for Shape from Contour
IEEE Transactions on Pattern Analysis and Machine Intelligence
A survey of 3d face recognition methods
AVBPA'05 Proceedings of the 5th international conference on Audio- and Video-Based Biometric Person Authentication
Meshes vs. depth maps in face recognition systems
ICAISC'12 Proceedings of the 11th international conference on Artificial Intelligence and Soft Computing - Volume Part I
Effect of facial feature points selection on 3d face shape reconstruction using regularization
ICONIP'12 Proceedings of the 19th international conference on Neural Information Processing - Volume Part V
Robust Bayesian fitting of 3D morphable model
Proceedings of the 10th European Conference on Visual Media Production
3D face sparse reconstruction based on local linear fitting
The Visual Computer: International Journal of Computer Graphics
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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.