Performance-driven facial animation
SIGGRAPH '90 Proceedings of the 17th annual conference on Computer graphics and interactive techniques
A Method for Registration of 3-D Shapes
IEEE Transactions on Pattern Analysis and Machine Intelligence - Special issue on interpretation of 3-D scenes—part II
Corner detection using bending value
Pattern Recognition Letters
Digital Image Processing
Robust Face Detection Using the Hausdorff Distance
AVBPA '01 Proceedings of the Third International Conference on Audio- and Video-Based Biometric Person Authentication
Real-Time Facial Animation based upon a Bank of 3D Facial Expressions
CA '98 Proceedings of the Computer Animation
Ear Biometrics Using 2D and 3D Images
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Workshops - Volume 03
Automatic Feature Extraction for Multiview 3D Face Recognition
FGR '06 Proceedings of the 7th International Conference on Automatic Face and Gesture Recognition
Robust lip region segmentation for lip images with complex background
Pattern Recognition
Locating and extracting the eye in human face images
Pattern Recognition
The caesar project: a 3-D surface anthropometry survey
3DIM'99 Proceedings of the 2nd international conference on 3-D digital imaging and modeling
Performance driven facial animation by appearance based tracking
IbPRIA'05 Proceedings of the Second Iberian conference on Pattern Recognition and Image Analysis - Volume Part I
MPEG-4 facial animation technology: survey, implementation, and results
IEEE Transactions on Circuits and Systems for Video Technology
An automatic method for computerized head and facial anthropometry
ICDHM'11 Proceedings of the Third international conference on Digital human modeling
Hi-index | 0.00 |
Facial anthropometry plays an important role in ergonomic applications. Most ergonomically designed products depend on stable and accurate human body measurement data. Our research automatically identifies human facial features based on three-dimensional geometric relationships, revealing a total of 67 feature points and 24 feature lines - more than the definitions associated with MPEG-4. In this study, we also verify the replicability, robustness, and accuracy of this feature set. Even with a lower-density point cloud from a non-dedicated head scanner, this method can provide robust results, with 86.6% validity in the 5 mm range. We also analyze the main 31 feature points on the human face, with 96.7% validity of less than 5 mm.