Model-based invariants for 3-D vision
International Journal of Computer Vision
Face Recognition Using Component-Based SVM Classification and Morphable Models
SVM '02 Proceedings of the First International Workshop on Pattern Recognition with Support Vector Machines
Face Recognition Based on Fitting a 3D Morphable Model
IEEE Transactions on Pattern Analysis and Machine Intelligence
Expression-invariant 3D face recognition
AVBPA'03 Proceedings of the 4th international conference on Audio- and video-based biometric person authentication
IEEE Transactions on Circuits and Systems for Video Technology
The 3DID face alignment system for verifying identity
Image and Vision Computing
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Facial image analysis is very useful in many applications such as video compression, talking heads, or biometrics. During the last few years, many algorithms have been proposed in particular for face recognition using classical 2-D images. Face is fairly easy to use and well accepted by people but generally not robust enough to be used in most practical security applications because too sensitive to variations in pose and illumination. One possibility to overcome this limitation is to work in 3-D instead of 2-D. But 3-D is costly and more difficult to manipulate and then ineffective to authenticate people in most contexts. Hence, to solve this problem, we propose a novel face recognition approach that is based on an asymmetric protocol: enrolment in 3-D but identification performed from 2-D images. So that, the goal is to make more robust face recognition while keeping the system practical. To make this 3-D/2-D approach possible, we introduce geometric invariants used in computer vision within the context of face recognition. We report preliminary experiments to evaluate robustness of invariants according to pose variations and to the accuracy of detection of facial feature points. Preliminary results obtained in terms of identification rate are encouraging.