Reconstruction of 3D Face Surface from Slices: A Literature Survey Mahmudul Hasan CPSC 601.20: Biometric Technologies Department of Computer Science, University of Calgary 2500 University Drive NW, Calgary, AB T2N 1N4, Canada email@example.com Table of Contents Introduction
CPSC 601.20: Biometric Technologies
Department of Computer Science, University of Calgary
2500 University Drive NW, Calgary, AB T2N 1N4, Canada
in terms of their basic methodologies and performance issues.
reconstruction of 3D faces.
requirement of prior knowledge about the class of solutions.
face surface reconstruction techniques.
technologies after 30 years of research .
illumination and/or pose remains a largely unsolved problem .
normalization increase the performance of face recognition.
pose invariant face recognition.
(range data) or reconstructed using shape from shading .
interesting and difficult problems in computer graphics .
scanning of people’s faces .
of work is required prior to animating the model .
reconstruct the 3D face from one or more 2D face images (slices).
solving the shape-from-shading problem .
face from a 2D face image [9, 10].
the appearance of the face in the image .
solving a generalized eigenvector problem .
sample points [15, 16, 17] and labeled image regions .
given one or more 2D face images.
illumination, pose, and facial expression.
database or for the purpose of face recognition.
used to generate the new views to any pose .
burn victims .
using only the shading information .
surface normal at that point and the incident light ray .
approximation to many surfaces including human skin .
maps based on Markov Random Fields (MWFs) .
posteriori estimation of the disparity maps .
eliminated by epipolar constraint check .
by one pairwise MRF .
(BP) iterations on the MRF .
eliminate outliers .
Fig. 1. Face image triplet 
Fig. 2. Reconstructed 3D face 
uses prior knowledge about the class of solutions .
space of general textured surfaces of a given topology .
space representation .
done simultaneously in an analysis-by-synthesis loop .
image in terms of pixel wise image difference .
Fig. 3. The top row shows the reconstructions of 3D shape and texture. In the second row, results are rendered into the original images with pose and illumination recovered by the algorithm. The third row shows novel views .
the missing vertex coordinates .
components have been used .
achieved with 15 to 20 points .
average texture of the morphable model .
Fig. 4. From an original image at unknown pose (top, left) and a frontal starting position (top, right), the algorithm estimates 3D shape and pose from 17 feature coordinates, including 7 directional constraints (second row). 140 principal components and 7 vectors for transformations were used. The third row shows the texture-mapped result. Computation time is 250ms [9,10].
from six different views .
applied to reconstruct a personalized 3D model of the face .
be obtained from a single image directly .
function that measures the matching cost of input images .
the 3D surface reconstruction algorithm .
Fig. 5. Synchronized images captured from six views 
Fig. 6. (a) 2D face alignment; (b) Initial 3D shape estimated from the 2D facial feature points; (c) The deformable mesh; (d) Result of 3D surface reconstruction; (e) The 3D shape estimated from 3D points 
has been conducted under this study.
the input 2D images.
more towards uncontrolled imaging conditions.
handle the uncontrolled imaging conditions most promisingly.
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