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A Survey of 3D Face Recognition Methods (2005)
| Venue: | In Lecture Notes in Computer Science |
| Citations: | 29 - 2 self |
BibTeX
@INPROCEEDINGS{Scheenstra05asurvey,
author = {Alize Scheenstra and Arnout Ruifrok and Remco C. Veltkamp},
title = {A Survey of 3D Face Recognition Methods},
booktitle = {In Lecture Notes in Computer Science},
year = {2005},
pages = {891--899},
publisher = {SpringerVerlag}
}
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Abstract
Abstract. Many researches in face recognition have been dealing with the challenge of the great variability in head pose, lighting intensity and direction,facial expression, and aging. The main purpose of this overview is to describe the recent 3D face recognition algorithms. The last few years more and more 2D face recognition algorithms are improved and tested on less than perfect images. However, 3D models hold more information of the face, like surface information, that can be used for face recognition or subject discrimination. Another major advantage is that 3D face recognition is pose invariant. A disadvantage of most presented 3D face recognition methods is that they still treat the human face as a rigid object. This means that the methods aren’t capable of handling facial expressions. Although 2D face recognition still seems to outperform the 3D face recognition methods, it is expected that this will change in the near future. 1
Keyphrases
face recognition method face recognition facial expression head pose perfect image near future main purpose surface information major advantage rigid object pose invariant 2d face recognition algorithm many research great variability subject discrimination last year human face face recognition algorithm
