Face verification from 3D and grey level clues
Pattern Recognition Letters
Content based retrieval of VRML objects: an iterative and interactive approach
Proceedings of the sixth Eurographics workshop on Multimedia 2001
ICPR '96 Proceedings of the International Conference on Pattern Recognition (ICPR '96) Volume III-Volume 7276 - Volume 7276
Multi-Modal 2D and 3D Biometrics for Face Recognition
AMFG '03 Proceedings of the IEEE International Workshop on Analysis and Modeling of Faces and Gestures
Face recognition: A literature survey
ACM Computing Surveys (CSUR)
A Survey Of Approaches To Three-Dimensional Face Recognition
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 1 - Volume 01
Expression-invariant 3D face recognition
AVBPA'03 Proceedings of the 4th international conference on Audio- and video-based biometric person authentication
Face authentication based on multiple profiles extracted from range data
AVBPA'03 Proceedings of the 4th international conference on Audio- and video-based biometric person authentication
Intelligent 3D Face Recognition
ISICA '08 Proceedings of the 3rd International Symposium on Advances in Computation and Intelligence
Towards 3D-aided profile-based face recognition
BTAS'09 Proceedings of the 3rd IEEE international conference on Biometrics: Theory, applications and systems
Profile-based 3D-aided face recognition
Pattern Recognition
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With the rapid development of 3D imaging technology, face recognition using 3D range data has become another alternative in the field of biometrics. Unlike face recognition using 2D intensity images, which has been studied intensively by many researchers since the 1960’s, 3D range data records the exact geometry of a person and it is invariant with respect to illumination changes of the environment and orientation changes of the person. This paper proposes a new algorithm to register and identify 3D range faces. Profiles and contours are extracted for the matching of a probe face with available gallery faces. Different combinations of profiles are tried for the purpose of face recognition using a set of 27 subjects. Our results show that the central vertical profile is one of the most powerful profiles to characterize individual faces and that the contour is also a potentially useful feature for face recognition.