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
Linear Object Classes and Image Synthesis From a Single Example Image
IEEE Transactions on Pattern Analysis and Machine Intelligence
Separability of Pose and Expression in Facial Tracking and Animation
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
Face Recognition Based on Fitting a 3D Morphable Model
IEEE Transactions on Pattern Analysis and Machine Intelligence
Efficient, Robust and Accurate Fitting of a 3D Morphable Model
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Matching 2.5D Face Scans to 3D Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
ACM SIGGRAPH 2006 Papers
Journal of Cognitive Neuroscience
Evaluation of 3d face recognition using registration and PCA
AVBPA'05 Proceedings of the 5th international conference on Audio- and Video-Based Biometric Person Authentication
Fast and automatic video object segmentation and tracking for content-based applications
IEEE Transactions on Circuits and Systems for Video Technology
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In this paper, we developed a biometric user authentication system based on 3-dimensional (3D) face recognition under ubiquitous computing environment. Since 2D based face recognition has been shown its structural limitation, 3D model based approach for face recognition has been spotlighted as a robust solution under variant conditions of pose and illumination. Since 3D face model consists of a large number of vertices, 3D model based face recognition system is generally inefficient for real-time computation. We propose a novel 3D face representation algorithm to reduce the number of vertices and optimize its computation time while maintaining reasonable recognition performance. We evaluate the performance of proposed algorithm with the Korean face database collected using a stereo-vision based 3D face capturing device. Additionally, various decision making similarity measures were explored for recognition performance. Our experimental results indicated that our proposed algorithm is robust for biometric user authentication and is also reasonably fast for real-time processing.