Joint and implicit registration for face recognition

Contemporary face recognition algorithms rely on precise localization of keypoints (corner of eye, nose etc.). Unfortunately, finding keypoints reliably and accurately remains a hard problem. In this paper we pose two questions. First, is it possible to exploit the gallery image in order to find keypoints in the probe image? For instance, consider finding the left eye in the probe image. Rather than using a generic eye model, we use a model that is informed by the appearance of the eye in the gallery image. To this end we develop a probabilistic model which combines recognition and keypoint localization. Second, is it necessary to localize keypoints? Alternatively we can consider keypoint position as a hidden variable which we marginalize over in a Bayesian manner. We demonstrate that both of these innovations improve performance relative to conventional methods in both frontal and cross-pose face recognition.

[1]  David J. Kriegman,et al.  Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection , 1996, ECCV.

[2]  Alexander M. Bronstein,et al.  Expression-Invariant Representations of Faces , 2007, IEEE Transactions on Image Processing.

[3]  Azriel Rosenfeld,et al.  Face recognition: A literature survey , 2003, CSUR.

[4]  Yuan Yan Tang,et al.  Topology Preserving Non-negative Matrix Factorization for Face Recognition , 2008, IEEE Transactions on Image Processing.

[5]  Wen Gao,et al.  Unified Principal Component Analysis with generalized Covariance Matrix for face recognition , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[6]  Jian-Huang Lai,et al.  Face illumination normalization on large and small scale features , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[7]  Mohammad H. Mahoor,et al.  Facial features extraction in color images using enhanced active shape model , 2006, 7th International Conference on Automatic Face and Gesture Recognition (FGR06).

[8]  Josef Kittler,et al.  Image Feature Localization by Multiple Hypothesis Testing of Gabor Features , 2008, IEEE Transactions on Image Processing.

[9]  Jonathan Warrell,et al.  Tied Factor Analysis for Face Recognition across Large Pose Differences , 2008, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[10]  Aleix M. Martínez,et al.  Recognizing Imprecisely Localized, Partially Occluded, and Expression Variant Faces from a Single Sample per Class , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[11]  Yuxiao Hu,et al.  Face recognition using Laplacianfaces , 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[12]  Timothy F. Cootes,et al.  Active Appearance Models , 2001, IEEE Trans. Pattern Anal. Mach. Intell..

[13]  Liya Ding,et al.  Precise detailed detection of faces and facial features , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[14]  Andrew Zisserman,et al.  Regression and classification approaches to eye localization in face images , 2006, 7th International Conference on Automatic Face and Gesture Recognition (FGR06).

[15]  Patrick J. Flynn,et al.  Overview of the face recognition grand challenge , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[16]  Huan Wang,et al.  Misalignment-robust face recognition , 2008, CVPR.

[17]  Jian Yang,et al.  KPCA plus LDA: a complete kernel Fisher discriminant framework for feature extraction and recognition , 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[18]  Kin-Man Lam,et al.  Illumination invariant face recognition , 2005, Pattern Recognit..

[19]  Xudong Jiang,et al.  Eigenfeature Regularization and Extraction in Face Recognition , 2008, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[20]  Sergey Ioffe,et al.  Probabilistic Linear Discriminant Analysis , 2006, ECCV.

[21]  James H. Elder,et al.  Probabilistic Linear Discriminant Analysis for Inferences About Identity , 2007, 2007 IEEE 11th International Conference on Computer Vision.

[22]  Aristodemos Pnevmatikakis,et al.  Impact of Face Registration Errors on Recognition , 2006, AIAI.

[23]  Alex Pentland,et al.  Bayesian face recognition , 2000, Pattern Recognit..

[24]  D. Rubin,et al.  Maximum likelihood from incomplete data via the EM - algorithm plus discussions on the paper , 1977 .

[25]  Alex Pentland,et al.  Face recognition using eigenfaces , 1991, Proceedings. 1991 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.