On the simultaneous recognition of identity and expression from BU-3DFE datasets
Pattern Recognition Letters
3D human face description: landmarks measures and geometrical features
Image and Vision Computing
3D/4D facial expression analysis: An advanced annotated face model approach
Image and Vision Computing
Face recognition using facial symmetry
Proceedings of the Second International Conference on Computational Science, Engineering and Information Technology
3D facial landmark localization using combinatorial search and shape regression
ECCV'12 Proceedings of the 12th international conference on Computer Vision - Volume Part I
Superfaces: a super-resolution model for 3d faces
ECCV'12 Proceedings of the 12th international conference on Computer Vision - Volume Part I
Robust learning from normals for 3d face recognition
ECCV'12 Proceedings of the 12th international conference on Computer Vision - Volume 2
Computer Vision and Image Understanding
Geometric histograms of 3D keypoints for face identification with missing parts
3DOR '13 Proceedings of the Sixth Eurographics Workshop on 3D Object Retrieval
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The uncontrolled conditions of real-world biometric applications pose a great challenge to any face recognition approach. The unconstrained acquisition of data from uncooperative subjects may result in facial scans with significant pose variations along the yaw axis. Such pose variations can cause extensive occlusions, resulting in missing data. In this paper, a novel 3D face recognition method is proposed that uses facial symmetry to handle pose variations. It employs an automatic landmark detector that estimates pose and detects occluded areas for each facial scan. Subsequently, an Annotated Face Model is registered and fitted to the scan. During fitting, facial symmetry is used to overcome the challenges of missing data. The result is a pose invariant geometry image. Unlike existing methods that require frontal scans, the proposed method performs comparisons among interpose scans using a wavelet-based biometric signature. It is suitable for real-world applications as it only requires half of the face to be visible to the sensor. The proposed method was evaluated using databases from the University of Notre Dame and the University of Houston that, to the best of our knowledge, include the most challenging pose variations publicly available. The average rank-one recognition rate of the proposed method in these databases was 83.7 percent.