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Alignment by Maximization of Mutual Information
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The FERET Evaluation Methodology for Face-Recognition Algorithms
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Detecting Faces in Images: A Survey
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Experiments on Eigenfaces Robustness
ICPR '02 Proceedings of the 16 th International Conference on Pattern Recognition (ICPR'02) Volume 1 - Volume 1
Lucas-Kanade 20 Years On: A Unifying Framework
International Journal of Computer Vision
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Automatic Eye Detection and Its Validation
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Workshops - Volume 03
Data Driven Image Models through Continuous Joint Alignment
IEEE Transactions on Pattern Analysis and Machine Intelligence
Mutual Information for Lucas-Kanade Tracking (MILK): An Inverse Compositional Formulation
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An iterative image registration technique with an application to stereo vision
IJCAI'81 Proceedings of the 7th international joint conference on Artificial intelligence - Volume 2
Face recognition with patterns of oriented edge magnitudes
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part I
Facial contour labeling via congealing
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part I
Learning object detection from a small number of examples: the importance of good features
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Intensity-Based Congealing for Unsupervised Joint Image Alignment
ICPR '10 Proceedings of the 2010 20th International Conference on Pattern Recognition
Deformable Model Fitting by Regularized Landmark Mean-Shift
International Journal of Computer Vision
Curse of mis-alignment in face recognition: problem and a novel mis-alignment learning solution
FGR' 04 Proceedings of the Sixth IEEE international conference on Automatic face and gesture recognition
SCface --- surveillance cameras face database
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Face alignment using statistical models and wavelet features
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
Regression based automatic face annotation for deformable model building
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An online three-stage method for facial point localization
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Joint face alignment with a generic deformable face model
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
Enhanced Patterns of Oriented Edge Magnitudes for Face Recognition and Image Matching
IEEE Transactions on Image Processing
Robust and efficient parametric face alignment
ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
RASL: Robust Alignment by Sparse and Low-Rank Decomposition for Linearly Correlated Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
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Entropy Congealing is an unsupervised joint image alignment method, in which the transformation parameters are obtained by minimizing a sum-of-entropy function. Our previous work presented a forward formulation of entropy Congealing to estimate all the transformation parameters at the same time. In this paper, we propose an inverse compositional Lucas-Kanade formulation of entropy Congealing. This yields constant parts in Jacobian and Hessian which can be precomputed to decrease the computational complexity. Moreover, we combine Congealing with POEM descriptor to catch more information about face. Experimental results indicate that the proposed algorithm performs better than other alignment methods, regarding several evaluation criteria on different databases. Concerning the complexity, the proposed algorithm is more efficient than other considered approaches. Also, compared to the forward formulation, the inverse method produces a speed improvement of 20%.