Pose invariant face recognition

We describe a novel neural network architecture, which can recognize human faces with any view in a certain viewing angle range (from left 30 degrees to right 30 degrees out of plane rotation). View-specific eigenface analysis is used as the front-end of the system to extract features, and the neural network ensemble is used for recognition. Experimental results show that the recognition accuracy of our network ensemble is higher than conventional methods such as using a single neural network to recognize faces of a specific view.

[1]  Lawrence Sirovich,et al.  Application of the Karhunen-Loeve Procedure for the Characterization of Human Faces , 1990, IEEE Trans. Pattern Anal. Mach. Intell..

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

[3]  Lars Kai Hansen,et al.  Ensemble methods for handwritten digit recognition , 1992, Neural Networks for Signal Processing II Proceedings of the 1992 IEEE Workshop.

[4]  Roberto Brunelli,et al.  Face Recognition: Features Versus Templates , 1993, IEEE Trans. Pattern Anal. Mach. Intell..

[5]  Eros Gian Alessandro Pasero,et al.  Multi-layer perceptron ensembles for increased performance and fault-tolerance in pattern recognition tasks , 1994, Proceedings of 1994 IEEE International Conference on Neural Networks (ICNN'94).

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

[7]  Alex Pentland,et al.  View-based and modular eigenspaces for face recognition , 1994, 1994 Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.

[8]  J. Kirkland.  Squad-based expert modules for closing diphthong recognition , 1995, Proceedings 1995 Second New Zealand International Two-Stream Conference on Artificial Neural Networks and Expert Systems.

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

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

[11]  Harry Wechsler,et al.  Face recognition using hybrid classifier systems , 1996, Proceedings of International Conference on Neural Networks (ICNN'96).

[12]  Harry Wechsler,et al.  Face recognition using ensembles of networks , 1996, Proceedings of 13th International Conference on Pattern Recognition.

[13]  Tomaso A. Poggio,et al.  Linear Object Classes and Image Synthesis From a Single Example Image , 1997, IEEE Trans. Pattern Anal. Mach. Intell..

[14]  Nathan Intrator,et al.  Classification of seismic signals by integrating ensembles of neural networks , 1998, IEEE Trans. Signal Process..

[15]  Timothy F. Cootes,et al.  Active Appearance Models , 1998, ECCV.

[16]  D. B. Graham,et al.  Face recognition from unfamiliar views: subspace methods and pose dependency , 1998, Proceedings Third IEEE International Conference on Automatic Face and Gesture Recognition.