Face detection using a mixture of factor analyzers

We present a probabilistic method to detect human faces using a mixture of factor analyzers. One characteristic of this mixture model is that it concurrently performs clustering and, within each cluster, local dimensionality reduction. A wide range of face images including ones in different poses, with different expressions and under different lighting conditions are used as the training set to capture the variations of human faces. In order to fit the mixture model to the sample face images, the parameters are estimated using an EM algorithm. Experimental results show that faces in different poses, with different facial expressions, and under different lighting conditions are accurately detected by our method.

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

[2]  H. Sebastian Seung,et al.  Learning the parts of objects by non-negative matrix factorization , 1999, Nature.

[3]  Teuvo Kohonen,et al.  The self-organizing map , 1990, Neurocomputing.

[4]  Syed A. Rizvi,et al.  The FERET Evaluation , 1998 .

[5]  M. Turk,et al.  Eigenfaces for Recognition , 1991, Journal of Cognitive Neuroscience.

[6]  Thomas S. Huang,et al.  Face detection with information-based maximum discrimination , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[7]  Brendan J. Frey,et al.  Mixtures of local linear subspaces for face recognition , 1998, Proceedings. 1998 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No.98CB36231).

[8]  Tomaso A. Poggio,et al.  Example-Based Learning for View-Based Human Face Detection , 1998, IEEE Trans. Pattern Anal. Mach. Intell..

[9]  Takeo Kanade,et al.  Rotation invariant neural network-based face detection , 1998, Proceedings. 1998 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No.98CB36231).

[10]  Shimon Ullman,et al.  Face Recognition: The Problem of Compensating for Changes in Illumination Direction , 1997, IEEE Trans. Pattern Anal. Mach. Intell..

[11]  Tomaso A. Poggio,et al.  Pedestrian detection using wavelet templates , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[12]  Rama Chellappa,et al.  Discriminant analysis of principal components for face recognition , 1998, Proceedings Third IEEE International Conference on Automatic Face and Gesture Recognition.

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

[14]  Geoffrey E. Hinton,et al.  The EM algorithm for mixtures of factor analyzers , 1996 .

[15]  Heekuck Oh,et al.  Neural Networks for Pattern Recognition , 1993, Adv. Comput..

[16]  Kah Kay Sung,et al.  Learning and example selection for object and pattern detection , 1995 .

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

[18]  Ferdinando Silvestro Samaria,et al.  Face recognition using Hidden Markov Models , 1995 .

[19]  Uday B. Desai,et al.  Finding faces in photographs , 1998, Sixth International Conference on Computer Vision (IEEE Cat. No.98CH36271).

[20]  Dorothy T. Thayer,et al.  EM algorithms for ML factor analysis , 1982 .

[21]  Federico Girosi,et al.  Training support vector machines: an application to face detection , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[22]  Thomas S. Huang,et al.  Object detection using hierarchical MRF and MAP estimation , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[23]  D. B. Gerham.  Characterizing virtual eigensignatures for general purpose face recognition , 1998 .

[24]  Takeo Kanade,et al.  A statistical method for 3D object detection applied to faces and cars , 2000, Proceedings IEEE Conference on Computer Vision and Pattern Recognition. CVPR 2000 (Cat. No.PR00662).

[25]  J. Schmee.  An Introduction to Multivariate Statistical Analysis , 1986 .

[26]  Narendra Ahuja,et al.  A SNoW-Based Face Detector , 1999, NIPS.

[27]  Takeo Kanade,et al.  Neural Network-Based Face Detection , 1998, IEEE Trans. Pattern Anal. Mach. Intell..

[28]  Richard O. Duda,et al.  Pattern classification and scene analysis , 1974, A Wiley-Interscience publication.

[29]  Takeo Kanade,et al.  Human Face Detection in Visual Scenes , 1995, NIPS.

[30]  Vicki Bruce,et al.  Face Recognition: From Theory to Applications , 1999 .

[31]  Juyang Weng,et al.  Using Discriminant Eigenfeatures for Image Retrieval , 1996, IEEE Trans. Pattern Anal. Mach. Intell..

[32]  D. B. Graham,et al.  Characterising Virtual Eigensignatures for General Purpose Face Recognition , 1998 .

[33]  Takeo Kanade,et al.  Probabilistic modeling of local appearance and spatial relationships for object recognition , 1998, Proceedings. 1998 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No.98CB36231).

[34]  Geoffrey E. Hinton,et al.  Modeling the manifolds of images of handwritten digits , 1997, IEEE Trans. Neural Networks.

[35]  Tomaso A. Poggio,et al.  A general framework for object detection , 1998, Sixth International Conference on Computer Vision (IEEE Cat. No.98CH36271).