Active shape models—their training and application
Computer Vision and Image Understanding
COSMOS-A Representation Scheme for 3D Free-Form Objects
IEEE Transactions on Pattern Analysis and Machine Intelligence
Use of depth and colour eigenfaces for face recognition
Pattern Recognition Letters
Empirical Performance Analysis of Linear Discriminant Classifiers
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
An Evaluation of Multimodal 2D+3D Face Biometrics
IEEE Transactions on Pattern Analysis and Machine Intelligence
Three-Dimensional Face Recognition
International Journal of Computer Vision
Matching 2.5D Face Scans to 3D Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
A 3D Facial Expression Database For Facial Behavior Research
FGR '06 Proceedings of the 7th International Conference on Automatic Face and Gesture Recognition
Multiple Nose Region Matching for 3D Face Recognition under Varying Facial Expression
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
An Efficient Multimodal 2D-3D Hybrid Approach to Automatic Face Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
3D human face description: landmarks measures and geometrical features
Image and Vision Computing
3D face recognition using local binary patterns
Signal Processing
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We present a novel approach to 3D face recognition using compact face signatures based on automatically detected 3D landmarks. We represent the face geometry with inter-landmark distances within selected regions of interest to achieve robustness to expression variations. The inter-landmark distances are compressed through Principal Component Analysis and Linear Discriminant Analysis is then applied on the reduced features to maximize the separation between face classes. The classification of a probe face is based on a nearest mean classifier after transforming the probe onto the subspace. We analyze the performance of different landmark combinations (signatures) to determine a signature that is robust to expressions. The selected signature is then used to train a Point Distribution Model for the automatic localization of the landmarks, without any prior knowledge of scale, pose, orientation or texture. We evaluate the proposed approach on a challenging publicly available facial expression database (BU-3DFE) and achieve 96.5% recognition rate using the automatically localized signature. Moreover, because of its compactness the face signature can be stored on 2D barcodes and used for radio-frequency identification.