Online Appearance Model Learning for Video-Based Face Recognition

In this paper, we propose a novel online learning method which can learn appearance models incrementally from a given video stream. The data of each frame in the video can be discarded as soon as it has been processed. We only need to maintain a few linear eigenspace models and a transition matrix to approximately construct face appearance manifolds. It is convenient to use these learnt models for video-based face recognition. There are mainly two contributions in this paper. First, we propose an algorithm which can learn appearance models online without using a pre-trained model. Second, we propose a method for eigenspace splitting to prevent that most samples cluster into the same eigenspace. This is useful for clustering and classification. Experimental results show that the proposed method can both learn appearance models online and achieve high recognition rate.

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

[2]  David J. Kriegman,et al.  Video-based face recognition using probabilistic appearance manifolds , 2003, 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings..

[3]  Trevor Darrell,et al.  Face Recognition from Long-Term Observations , 2002, ECCV.

[4]  Dit-Yan Yeung,et al.  Face recognition with image sets using hierarchically extracted exemplars from appearance manifolds , 2006, 7th International Conference on Automatic Face and Gesture Recognition (FGR06).

[5]  Roberto Cipolla,et al.  Face Recognition from Video Using the Generic Shape-Illumination Manifold , 2006, ECCV.

[6]  Tsuhan Chen,et al.  Video-based face recognition using adaptive hidden Markov models , 2003, 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings..

[7]  Ken-ichi Maeda,et al.  Face recognition using temporal image sequence , 1998, Proceedings Third IEEE International Conference on Automatic Face and Gesture Recognition.

[8]  Rama Chellappa,et al.  Probabilistic identity characterization for face recognition , 2004, Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004. CVPR 2004..

[9]  Ralph R. Martin,et al.  Incremental Eigenanalysis for Classification , 1998, BMVC.

[10]  Paul A. Viola,et al.  Rapid object detection using a boosted cascade of simple features , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.

[11]  Alex Pentland,et al.  Probabilistic Visual Learning for Object Representation , 1997, IEEE Trans. Pattern Anal. Mach. Intell..

[12]  David J. Kriegman,et al.  Online learning of probabilistic appearance manifolds for video-based recognition and tracking , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[13]  Wladyslaw Skarbek.  Merging Subspace Models for Face Recognition , 2003, CAIP.

[14]  Zhifeng Li,et al.  Spatio-temporal Embedding for Statistical Face Recognition from Video , 2006, ECCV.

[15]  Alessandra Lumini,et al.  Eigenspace merging for model updating , 2002, Object recognition supported by user interaction for service robots.

[16]  Rama Chellappa,et al.  Probabilistic recognition of human faces from video , 2002, Proceedings. International Conference on Image Processing.

[17]  Ralph R. Martin,et al.  Merging and Splitting Eigenspace Models , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[18]  Zhifeng Li,et al.  Frame synchronization and multi-level subspace analysis for video based face recognition , 2004, Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004. CVPR 2004..