3D Human Face Recognition Using Point Signature
FG '00 Proceedings of the Fourth IEEE International Conference on Automatic Face and Gesture Recognition 2000
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ACM Computing Surveys (CSUR)
An Evaluation of Multimodal 2D+3D Face Biometrics
IEEE Transactions on Pattern Analysis and Machine Intelligence
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CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Three-Dimensional Face Recognition Using Shapes of Facial Curves
IEEE Transactions on Pattern Analysis and Machine Intelligence
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ICASSP '01 Proceedings of the Acoustics, Speech, and Signal Processing, 2001. on IEEE International Conference - Volume 03
Illumination Invariant Face Recognition Using Near-Infrared Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
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International Journal of Computer Vision
An Expression Deformation Approach to Non-rigid 3D Face Recognition
International Journal of Computer Vision
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3D Face Recognition Using Isogeodesic Stripes
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Shape Analysis of Elastic Curves in Euclidean Spaces
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IEEE Transactions on Image Processing
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There are many challenges to achieve 2D face recognition including illumination, expression, and pose variations. However, the human face provides not only 2D texture but also rich 3D shape information. In this work, we present a novel 3D face recognition approach based on a new proposed concept termed structured template in analogy with the structured light approach. Our approach excludes the non-rigid facial region which is most affected by facial expressions. We first apply the structured template on the facial range image to extract 20 levels of stripes and convert them to pointclouds. Then we can represent a 3D facial scan by 20 levels of 3D open curves. As a result we can match the shape of two facial scans by matching the shape of their corresponding open curves. An open curve analysis algorithm is applied to calculate the geodesic distance between a pair of open curves extracted from different faces. The geodesic distance is then used as a similarity measure and two facial scans can be matched using the sum of all levels of their corresponding geodesic distance. Experiments are performed on the FRGC v2.0 dataset which demonstrate excellent performance.