The DOI matches its Crossref record.
Objective 3D face recognition: Evolution, approaches and challenges
Abstract
Face recognition is a natural human ability and a widely accepted identification and authentication method. In modern legal settings, a lot of credence is placed on identifications made by eyewitnesses. Consequently these are based on human perception which is often flawed and can lead to situations where identity is disputed. Therefore, there is a clear need to secure identifications in an objective way based on anthropometric measures. Anthropometry has existed for many years and has evolved with each advent of new technology and computing power. As a result of this, face recognition methodology has shifted from a purely 2D image-based approach to the use of 3D facial shape. However, one of the main challenges still remaining is the non-rigid structure of the face, which can change permanently over varying time-scales and briefly with facial expressions. The majority of face recognition methods have been developed by scientists with a very technical background such as biometry, pattern recognition and computer vision. This article strives to bridge the gap between these communities and the forensic science end-users. A concise review of face recognition using 3D shape is given. Methods using 3D shape applied to data embodying facial expressions are tabulated for reference. From this list a categorization of different strategies to deal with expressions is presented. The underlying concepts and practical issues relating to the application of each strategy are given, without…
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“Second, ear shape does not change like facial shape due to facial expressions (Smeets et al. 2010).”
From An investigation of matching symmetry in the human pinnae with possible implications for 3D ear recognition and sound localization (2015) · cites this paper as Smeets et al. 2010
Citations
Cites
No references could be resolved yet.
Research works printed but not linked: 28
- Ortega-Garcia, 2004, Authentication gets personal with biometrics, IEEE Signal. Proc. Mag., 21, 50, 10.1109/MSP.2004.1276113 · DOI
- Poswillo, 1989, Myths, masks and mechanisms of facial deformity, Eur. J. Orhtodont., 11, 1, 10.1093/oxfordjournals.ejo.a035958 · DOI
- Liu, 1999, Lighting direction affects recognition of untextured faces in photographic positive and negative, Vision Res., 39, 4003, 10.1016/S0042-6989(99)00109-1 · DOI
- Bruce, 2001, Matching identities of familiar and unfamiliar faces caught on cctv images, JEP: Appl., 7, 207
- Pike, 1997, Recognizing moving faces: the relative contribution of motion and perspective view information, Vis. Cogn., 4, 409, 10.1080/713756769 · DOI
- Chellapa, 1995, Human and machine recognition of faces: a survey, Proc. IEEE, 83, 705, 10.1109/5.381842 · DOI
- Zhao, 2003, Face recognition: a literature survey, ACM Comput. Surv., 35, 399, 10.1145/954339.954342 · DOI
- Abate, 2007, 2d and 3d face recognition: a survey, Pattern Recogn. Lett., 28, 1885, 10.1016/j.patrec.2006.12.018 · DOI
- Bowyer, 2006, A survey of approaches and challenges in 3d and multi-modal 3d+2d face recognition, Comput. Vis. Image, 101, 1, 10.1016/j.cviu.2005.05.005 · DOI
- Hamouz, 2007, 3d imaging for safety and security, vol. 35
- Mian, 2007, Keypoint detection and local feature matching for textured 3d face recognition, Int. J. Comput. Vis., 79, 1, 10.1007/s11263-007-0085-5 · DOI
- Chang, 2005, An evaluation of multi-modal 2d+3d face biometrics, IEEE Trans. Pattern Anal., 27, 619, 10.1109/TPAMI.2005.70 · DOI
- Tsalakanidou, 2003, Use of depth and colour eigenfaces for face recognition, Pattern Recogn. Lett., 1427, 10.1016/S0167-8655(02)00383-5 · DOI
- Richtsmeier, 2002, The promise of geometric morphometrics, Am. J Phys. Anthropol., 35, 63, 10.1002/ajpa.10174 · DOI
- Mpiperis, 2007, 3-d face recognition with the geodesic polar representation, IEEE Trans. Inf. Forensics Security, 2, 537, 10.1109/TIFS.2007.902326 · DOI
- Samir, 2006, Three-dimensional face recognition using shapes of facial curves, IEEE Trans. Pattern Anal., 28, 1858, 10.1109/TPAMI.2006.235 · DOI
- Besl, 1992, A method for registration of 3-d shapes, IEEE Trans. Pattern Anal., 14, 239, 10.1109/34.121791 · DOI
- Lu, 2006, Matching 2.5d face scans to 3d models, IEEE Trans. Pattern Anal., 28, 31, 10.1109/TPAMI.2006.15 · DOI
- Chang, 2006, Multiple nose region matching for 3d face recognition under varying facial expression, IEEE Trans. Pattern Anal., 28, 1695, 10.1109/TPAMI.2006.210 · DOI
- Lele, 1991, Euclidean distance matrix analysis: a coordinate-free approach for comparing biological shapes using landmark data, Am. J. Phys. Anthropol., 86, 415, 10.1002/ajpa.1330860307 · DOI
- Chang, 2006, Multiple nose region matching for 3d face recognition under varying facial expression, IEEE Trans. Pattern Anal., 28, 1695, 10.1109/TPAMI.2006.210 · DOI
- Mpiperis, 2008, Bilinear models for 3-d face and facial expression recognition, IEEE Trans. Inf. Forensics Security, 3, 498, 10.1109/TIFS.2008.924598 · DOI
- J. Phillips, P. Grother, R. Micheals, D. Blackburn, E. Tabassi, M. Bone. Facial recognition vendor test 2002: Evaluation report, 2003, available from: http://www.frvt2002.org/FRVT2002/documents.htm. · DOI
- Bronstein, 2007, Expression-invariant representations of faces, IEEE Trans. Image Process., 16, 188, 10.1109/TIP.2006.884940 · DOI
- Heseltine, 2008, Three-dimensional face recognition using combinations of surface feature map subspace components, Image Vis. Comput., 26, 382, 10.1016/j.imavis.2006.12.008 · DOI
- Mian, 2007, An efficient multimodal 2d-3d hybrid approach to automatic face recognition, IEEE Trans. Pattern Anal., 29, 1927, 10.1109/TPAMI.2007.1105 · DOI
- Faltemier, 2008, A region ensemble for 3-d face recognition, IEEE Trans. Inf. Forensics Security, 3, 62, 10.1109/TIFS.2007.916287 · DOI
- Kakadiaris, 2007, Three-dimensional face recognition in the presence of facial expressions: an annotated deformable model approach, IEEE Trans. Pattern Anal., 29, 640, 10.1109/TPAMI.2007.1017 · DOI
Cited by
Sorted by citations
Claes, P., Reijniers, J., Shriver, M. D., Snyders, J., Suetens, P., Nielandt, J., De Tré, G., & Vandermeulen, D. (2015) · Journal of Anatomy · doi:10.1111/joa.12252 · Cited by 32
Self-citation
Metadata
- Authors
- Smeets, D., Claes, P., Vandermeulen, D., & Clement, J. G.
- Venue
- Forensic Science International
- Publisher
- Elsevier BV
- Published
- 2010-09
- Volume
- 201
- Issue
- 1-3
- Pages
- 125-132
- ISSN
- 0379-0738
- Cited by
- 90
- References
- 0
- Language
- en
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review