2D and 3D face recognition: A survey

By Andrea Francesco Abate, Michele Nappi, D. Riccio, Gabriele Sabatino. Pattern Recognition Letters, 2007

990 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Signal Processing

View Paper: https://www.semanticscholar.org/paper/2f5d44dc3e1b5955942133ff872ebd31716ec604

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. Matthias Nießner
2. Christian Theobalt
3. Michael Zollhöfer
4. Justus Thies
5. Michael J. Black
6. Luc Van Gool
7. Patrick Pérez
8. Andrew Zisserman

In Signal Processing

1. Almas Abbasi
2. Woo Chaw Seng
3. J. Unar
4. Mei Wang
5. Weihong Deng
6. Mislav Grgic
7. Sonja Grgic
8. Kresimir Delac

Citations in the corpus

• State of the Art on Monocular 3D Face Reconstruction, Tracking, and Applications (2018, background): “Finally, driven by applications in security and person identification, there is a large body of work on face recognition [ZCPR03, ANRS07], and more specifically expression recognition [SZPY12], which aims to advance user experiences in human-computer interaction scenarios.”
• A Survey of Face Recognition Techniques (2009, methodology): “The following discussion sheds some light on the methods in each category and attempts to give an idea of some of the benefits and drawbacks of the schemes mentioned therein in general (for detailed surveys, please see [44, 45]).”
• SCface – surveillance cameras face database (2011, background): “Many papers are written and many real-world systems are being developed and distributed [1, 12, 19, 22].”
• KinectFaceDB: A Kinect Database for Face Recognition (2014, background): “Recent surveys [25], [26] have suggested that face recognition methods exploiting 3-D cues are more efficient than”
• Understanding OSN-based facial disclosure against face authentication systems (2014, methodology): “In face recognition, holistic approaches and local landmark based approaches are the two major types of popular face recognition algorithms [1, 43].”
• Face recognition across pose: A review (2009, background): “More complete reviews on general face recognition algorithms can be found in [1,21,89].”
• Deep Face Recognition: A Survey (2018, background): “Over the past few years, there have been several surveys on FR [222], [18], [3], [78], [136] and its subdomains, including illumination-invariant FR [234], 3D FR [136], pose-invariant FR [216] and so on.”
• A review of biometric technology along with trends and prospects (2014, methodology): “An automated face recognition system establishes the identity of an individual based on the computation of 2D or 3D features (refer table 2) [84, 85].”