A Generalized EM Approach for 3D Model Based Face Recognition under Occlusions

This paper describes an algorithm for pose and illumination invariant face recognition from a single image under occlusions. The method iteratively estimates the parameters of a 3D morphable face model to approximate the appearance of a face in an image. Simultaneously, a visibility map is computed which segments the image into visible and occluded regions. The visibility map is incorporated into a probabilistic image formation model as a set of spatially correlated random variables. This leads to a Generalized Expectation-Maximization algorithm in which the estimation of the morphable model related parameters is interleaved with visibility computations. The validity of the algorithm is verified by a face recognition experiment using images from the publicly available AR Face Database.

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

[2]  David Beymer,et al.  Face recognition under varying pose , 1994, 1994 Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.

[3]  David Beymer,et al.  Face recognition from one example view , 1995, Proceedings of IEEE International Conference on Computer Vision.

[4]  Aleix M. Martinez,et al.  The AR face database , 1998 .

[5]  David J. Kriegman,et al.  Illumination cones for recognition under variable lighting: faces , 1998, Proceedings. 1998 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No.98CB36231).

[6]  A. Martínez,et al.  The AR face databasae , 1998 .

[7]  Yasuyuki Saito,et al.  Estimation of eyeglassless facial images using principal component analysis , 1999, Proceedings 1999 International Conference on Image Processing (Cat. 99CH36348).

[8]  Matthew Turk,et al.  A Morphable Model For The Synthesis Of 3D Faces , 1999, SIGGRAPH.

[9]  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..

[10]  Seong-Whan Lee,et al.  Reconstruction of Partially Damaged Face Images Based on a Morphable Face Model , 2003, IEEE Trans. Pattern Anal. Mach. Intell..

[11]  Thomas Vetter,et al.  Face Recognition Based on Fitting a 3D Morphable Model , 2003, IEEE Trans. Pattern Anal. Mach. Intell..

[12]  Raghu Machiraju,et al.  Model-based 3D face capture with shape-from-silhouettes , 2003, 2003 IEEE International SOI Conference. Proceedings (Cat. No.03CH37443).

[13]  Ralph Gross,et al.  Appearance-based face recognition and light-fields , 2004, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[14]  P. Fua,et al.  Accurate face models from uncalibrated and ill-lit video sequences , 2004, Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004. CVPR 2004..

[15]  C. Strecha,et al.  Wide-baseline stereo from multiple views: A probabilistic account , 2004, Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004. CVPR 2004..

[16]  Ralph Gross,et al.  Constructing and Fitting Active Appearance Models With Occlusion , 2004, 2004 Conference on Computer Vision and Pattern Recognition Workshop.

[17]  Yuxiao Hu,et al.  Automatic 3D reconstruction for face recognition , 2004, Sixth IEEE International Conference on Automatic Face and Gesture Recognition, 2004. Proceedings..

[18]  F. Tarres,et al.  A novel method for face recognition under partial occlusion or facial expression variations , 2005, 47th International Symposium ELMAR, 2005..

[19]  Sang Chul Ahn,et al.  Glasses removal from facial image using recursive error compensation , 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[20]  Luc Van Gool,et al.  Parametric Stereo for Multi-pose Face Recognition and 3D-Face Modeling , 2005, AMFG.