A morphable model for the synthesis of 3D faces
Proceedings of the 26th annual conference on Computer graphics and interactive techniques
Distortion Invariant Object Recognition in the Dynamic Link Architecture
IEEE Transactions on Computers
Face Recognition Using Range Images
VSMM '97 Proceedings of the 1997 International Conference on Virtual Systems and MultiMedia
Real-Time Range Acquisition by Adaptive Structured Light
IEEE Transactions on Pattern Analysis and Machine Intelligence
Automatic 3D face verification from range data
ICME '03 Proceedings of the 2003 International Conference on Multimedia and Expo - Volume 3 (ICME '03) - Volume 03
Journal of Cognitive Neuroscience
Component-based face recognition with 3D morphable models
AVBPA'03 Proceedings of the 4th international conference on Audio- and video-based biometric person authentication
Face recognition using LDA-based algorithms
IEEE Transactions on Neural Networks
A Security Analysis of Biometric Template Protection Schemes
ICIAR '09 Proceedings of the 6th International Conference on Image Analysis and Recognition
Multi-algorithm fusion with template protection
BTAS'09 Proceedings of the 3rd IEEE international conference on Biometrics: Theory, applications and systems
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We present an automatic face recognition approach, which relies on the analysis of the three-dimensional facial surface. The proposed approach consists of two basic steps, namely a precise fully automatic normalization stage followed by a histogram-based feature extraction algorithm. During normalization the tip and the root of the nose are detected and the symmetry axis of the face is determined using a PCA analysis and curvature calculations. Subsequently, the face is realigned in a coordinate system derived from the nose tip and the symmetry axis, resulting in a normalized 3D model. The actual region of the face to be analyzed is determined using a simple statistical method. This area is split into disjoint horizontal subareas and the distribution of depth values in each subarea is exploited to characterize the face surface of an individual. Our analysis of the depth value distribution is based on a straightforward histogram analysis of each subarea. When comparing the feature vectors resulting from the histogram analysis we apply three different similarity metrics. The proposed algorithm has been tested with the FRGC v2 database, which consists of 4950 range images. Our results indicate that the city block metric provides the best classification results with our feature vectors. The recognition system achieved an equal error rate of 5.89% with correctly normalized face models.