Evaluation of 3D Face Recognition in the presence of facial expressions: an Annotated Deformable Model approach

Abstract

From a user’s perspective, face recognition is one of the most desirable biometrics, due to its non-intrusive nature; however, variables such as face expression tend to severely affect recognition rates. We have applied to this problem our previous work on elastically adaptive deformable models to obtain parametric representations of the geometry of selected localized face areas using an annotated face model. We then use wavelet analysis to extract a compact biometric signature, thus allowing us to perform rapid comparisons on either a global or a per area basis. To evaluate the performance of our algorithm, we have conducted experiments using data from the Face Recognition Grand Challenge data corpus, the largest and most established data corpus for face recognition currently available. Our results indicate that our algorithm exhibits high levels of accuracy and robustness, and is not gender biased. In addition, it is minimally affected by facial expressions.

DOI: 10.1109/CVPR.2005.573

Extracted Key Phrases

13 Figures and Tables

Statistics

01020'05'06'07'08'09'10'11'12'13'14'15'16'17
Citations per Year

78 Citations

Semantic Scholar estimates that this publication has 78 citations based on the available data.

See our FAQ for additional information.

Cite this paper

@article{Passalis2005EvaluationO3, title={Evaluation of 3D Face Recognition in the presence of facial expressions: an Annotated Deformable Model approach}, author={Georgios Passalis and Ioannis A. Kakadiaris and Theoharis Theoharis and George Toderici and Mohammed N. Murtuza}, journal={2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Workshops}, year={2005}, pages={171-171} }