Intraclass Retrieval of Nonrigid 3D Objects: Application to Face Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Viewpoint Invariant, Sparsely Registered, Patch Based, Face Verifier
International Journal of Computer Vision
Face recognition across pose: A review
Pattern Recognition
Efficient particle filtering using RANSAC with application to 3D face tracking
Image and Vision Computing
Probabilistic learning for fully automatic face recognition across pose
Image and Vision Computing
Face mosaicing for pose robust video-based recognition
ACCV'07 Proceedings of the 8th Asian conference on Computer vision - Volume Part II
Perfect histogram matching PCA for face recognition
Multidimensional Systems and Signal Processing
Plastic surgery: a new dimension to face recognition
IEEE Transactions on Information Forensics and Security
Face recognition based on 2D images under illumination and pose variations
Pattern Recognition Letters
Computer Vision and Image Understanding
Cross-pose face recognition based on partial least squares
Pattern Recognition Letters
Robust pose invariant face recognition using coupled latent space discriminant analysis
Computer Vision and Image Understanding
Pose invariant approach for face recognition at distance
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part VI
Viewpoint invariant matching via developable surfaces
ECCV'12 Proceedings of the 12th international conference on Computer Vision - Volume 2
Local Linear Regression on Hybrid Eigenfaces for Pose Invariant Face Recognition
International Journal of Computer Vision and Image Processing
Rough set based pose invariant face recognition with mug shot images
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology
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Researchers have been working on human face recognition for decades. Face recognition is hard due to different types of variations in face images, such as pose, illumination and expression, among which pose variation is the hardest one to deal with. To improve face recognition under pose variation, this paper presents a geometry assisted probabilistic approach. We approximate a human head with a 3D ellipsoid model, so that any face image is a 2D projection of such a 3D ellipsoid at a certain pose. In this approach, both training and test images are back projected to the surface of the 3D ellipsoid, according to their estimated poses, to form the texture maps. Thus the recognition can be conducted by comparing the texture maps instead of the original images, as done in traditional face recognition. In addition, we represent the texture map as an array of local patches, which enables us to train a probabilistic model for comparing corresponding patches. By conducting experiments on the CMU PIE database, we show that the proposed algorithm provides better performance than the existing algorithms.