2017
DOI: 10.3390/sym9100243
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Factors Affecting the Perception of 3D Facial Symmetry from 2D Projections

Abstract: Facial symmetry is believed to have an evolutionary significance and so its detection should be robust in natural settings. Previous studies of facial symmetry detection have used front views of faces where the decision could be made on 2D image properties rather than 3D facial properties. These studies also employed comparative judgements, which could be influenced by attractiveness rather than symmetry. Two experiments explored the ability to detect typical levels of 3D facial asymmetry (contrasted with whol… Show more

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Cited by 4 publications
(2 citation statements)
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References 48 publications
(65 reference statements)
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“…Previous studies have discussed the challenges when assessing nasal symmetry intraoperatively. Lewis et al 8 found that rotation of the face and asymmetric lighting can cause challenges in detecting facial asymmetry. This could play a role intraoperatively when it is challenging for the surgeon to be able to assess the face straight on without any rotation and with perfectly symmetric lighting.…”
Section: Discussionmentioning
confidence: 99%
“…Previous studies have discussed the challenges when assessing nasal symmetry intraoperatively. Lewis et al 8 found that rotation of the face and asymmetric lighting can cause challenges in detecting facial asymmetry. This could play a role intraoperatively when it is challenging for the surgeon to be able to assess the face straight on without any rotation and with perfectly symmetric lighting.…”
Section: Discussionmentioning
confidence: 99%
“…For face identification, Multi-View Whole Face (MVWF) images are synthesized to integrate real 3D facial feature information that boosts the face recognition accuracy. Motivated by the intrinsic symmetry of a face [37] (Figure 1(d)) exhibited by the LHF and RHF images (Figure 1 For performance evaluation of the proposed approach, four benchmark databases, namely, GavabDB [36], Bosphorus [38], UMB-DB [39], and FRGC v2.0 [40], have been used in this study. These databases carry pose and expression variations and are commonly utilized for developing 3D face recognition algorithms.…”
Section: Introductionmentioning
confidence: 99%