The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
Matching 2.5D face scans to 3D models
Abstract
The performance of face recognition systems that use two-dimensional images depends on factors such as lighting and subject's pose. We are developing a face recognition system that utilizes three-dimensional shape information to make the system more robust to arbitrary pose and lighting. For each subject, a 3D face model is constructed by integrating several 2.5D face scans which are captured from different views. 2.5D is a simplified 3D (x, y, z) surface representation that contains at most one depth value (z direction) for every point in the (x, y) plane. Two different modalities provided by the facial scan, namely, shape and texture, are utilized and integrated for face matching. The recognition engine consists of two components, surface matching and appearance-based matching. The surface matching component is based on a modified Iterative Closest Point (ICP) algorithm. The candidate list from the gallery used for appearance matching is dynamically generated based on the output of the surface matching component, which reduces the complexity of the appearance-based matching stage. Three-dimensional models in the gallery are used to synthesize new appearance samples with pose and illumination variations and the synthesized face images are used in discriminant subspace analysis. The weighted sum rule is applied to combine the scores given by the two matching components. Experimental results are given for matching a database of 200 3D face models with 598 2.5D independent test scans acquired under different pose and some lighting and expression changes. These results show the feasibility of the proposed matching scheme.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Turk, M., & Pentland, A. (1991) · Journal of Cognitive Neuroscience · doi:10.1162/jocn.1991.3.1.71 · Cited by 13,733
Martinez, A. M., & Kak, A. C. (2001) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.908974 · Cited by 3,249
Object modeling by registration of multiple range images
Chen, Y., & Medioni, G. (2002) · Proceedings. 1991 IEEE International Conference on Robotics and Automation · doi:10.1109/robot.1991.132043 · Cited by 1,409
Cited by
Mean squared error: Love it or leave it? A new look at Signal Fidelity Measures
Wang, Z., & Bovik, A. C. (2009) · IEEE Signal Processing Magazine · doi:10.1109/msp.2008.930649 · Cited by 3,175
An Efficient Multimodal 2D-3D Hybrid Approach to Automatic Face Recognition
Mian, A., Bennamoun, M., & Owens, R. (2007) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2007.1105 · Cited by 470
Multiple Nose Region Matching for 3D Face Recognition under Varying Facial Expression
Chang, K. I., Bowyer, K. W., & Flynn, P. J. (2006) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2006.210 · Cited by 359
3D Face Recognition Using Simulated Annealing and the Surface Interpenetration Measure
Queirolo, C. C., Silva, L., Bellon, O. R. P., & Segundo, M. P. (2010) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2009.14 · Cited by 201
Objective 3D face recognition: Evolution, approaches and challenges
Smeets, D., Claes, P., Vandermeulen, D., & Clement, J. G. (2010) · Forensic Science International · doi:10.1016/j.forsciint.2010.03.023 · Cited by 90
Metadata
- Authors
- Lu, X., Jain, A. K., & Colbry, D.
- Venue
- IEEE Transactions on Pattern Analysis and Machine Intelligence
- Published
- 2006-01
- Volume
- 28
- Issue
- 1
- Pages
- 31-43
- ISSN
- 0162-8828 (ISSN-L)
- OpenAlex
- W2161116336
- Cited by
- 339
- References
- 3
- FWCI
- 50.97
- Type
- journal article
- Language
- en
- Open access
- closed
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
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
