Multiple Nose Region Matching for 3D Face Recognition under Varying Facial Expression

By Kyong I. Chang, Kevin W. Bowyer, Patrick J. Flynn. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2006

410 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Signal Processing

View Paper: https://www.semanticscholar.org/paper/60aff22c049e1a26ef6ee12466165cf7e85a2b9e

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

1. Juergen Gall
2. Luc Van Gool
3. Ruigang Yang
4. Xiaoou Tang
5. Xiaogang Wang
6. Jitendra Malik
7. Chen Change Loy
8. Cordelia Schmid

In Signal Processing

1. Juergen Gall
2. Luc Van Gool
3. Matthias Dantone
4. Andrea Fossati
5. Gabriele Fanelli

Citations in the corpus

• A Survey on Human Motion Analysis from Depth Data (2013, background): “For example, the authors of [84, 85, 86] all use curvature to roughly localize the inner corners of the eyes.”
• Random Forests for Real Time 3D Face Analysis (2012, methodology): “(2010), Chang et al. (2006) all use curvature to roughly localize the inner corners of the eyes. Such an approach is very sensitive to missing depth data, particularly for the regions around the inner eye corners, frequently occluded by shadows. Also Mehryar et al. (2010) use surface curvatures by f”
• Robust semantic analysis by synthesis of 3D facial motion (2011, background): “In contrast, 3D part-based approaches receive growing attention for 3D face feature tracking [6], and for direct 3D expression recognition purposes [8], but also for 2D recognition and automatic facial behavior recognition from video, based on Facial Action Units [18].”