Partially occluded face completion and recognition

This paper proposes a spectral graph based algorithm for face image repairing, which can improve the recognition performance on occluded faces. Our algorithm is called ‘guided label-learning’, so named from graphical models, and can achieve a high-quality repairing of damaged or occluded faces. We apply our face repairing algorithm in order to produce completed faces, and then use face recognition to evaluate the performance of our algorithm. Experiment results show that, at most, a nearly 30%-increase in the recognition rate can be achieved for occluded faces with the use of our algorithm.

[1]  Patrick Pérez,et al.  Region filling and object removal by exemplar-based image inpainting , 2004, IEEE Transactions on Image Processing.

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

[3]  David Salesin,et al.  Interactive digital photomontage , 2004, SIGGRAPH 2004.

[4]  R. Basri,et al.  Shape representation and classification using the Poisson equation , 2004, CVPR 2004.

[5]  Zihan Zhou,et al.  Demo: Robust face recognition via sparse representation , 2008, 2008 8th IEEE International Conference on Automatic Face & Gesture Recognition.

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

[7]  Alexei A. Efros,et al.  Scene completion using millions of photographs , 2008, Commun. ACM.

[8]  Delbert Dueck,et al.  Clustering by Passing Messages Between Data Points , 2007, Science.

[9]  Guillermo Sapiro,et al.  Simultaneous structure and texture image inpainting , 2003, IEEE Trans. Image Process..

[10]  Kin-Man Lam,et al.  Face recognition using elastic local reconstruction based on a single face image , 2008, Pattern Recognit..

[11]  Allen Y. Yang,et al.  Robust Face Recognition via Sparse Representation , 2009, IEEE Transactions on Pattern Analysis and Machine Intelligence.