Face Detection Using Mixture of MLP Experts
Neural Processing Letters
Computer Vision and Image Understanding
Applications of high dimensionalmodel representations to computer vision
WSEAS Transactions on Mathematics
Applications of high dimensional model representations to computer vision
MAASE'09 Proceedings of the 2nd WSEAS international conference on Multivariate analysis and its application in science and engineering
Applications of flexibly initialized high dimensional model representation in computer vision
SMO'09 Proceedings of the 9th WSEAS international conference on Simulation, modelling and optimization
Integration of a human face annotation technology in an audio-visual search engine platform
Proceedings of the 2010 ACM Symposium on Applied Computing
A robust hybrid method for nonrigid image registration
Pattern Recognition
Efficient Detection and Recognition of 3D Ears
International Journal of Computer Vision
More on weak feature: self-correlate histogram distances
PSIVT'11 Proceedings of the 5th Pacific Rim conference on Advances in Image and Video Technology - Volume Part I
Hybrid machine learning approach for object recognition: fusion of features and decisions
Machine Graphics & Vision International Journal
Comparative analysis of benchmark datasets for face recognition algorithms verification
ICCVG'12 Proceedings of the 2012 international conference on Computer Vision and Graphics
Classification of sign-based image representations based on distance functions
Pattern Recognition and Image Analysis
Nearest neighbor weighted average customization for modeling faces
Machine Vision and Applications
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We present a system for pose and illumination invariant face recognition that combines two recent advances in the computer vision field: 3D morphable models and component-based recognition. A 3D morphable model is used to compute 3D face models from three input images of each subject in the training database. The 3D models are rendered under varying pose and illumination conditions to build a large set of synthetic images. These images are then used for training a component-based face recognition system. The face recognition module is preceded by a fast hierarchical face detector resulting in a system that can detect and identify faces in video images at about 4 Hz. The system achieved a recognition rate of 88% on a database of 2000 real images of ten people, which is significantly better than a comparable global face recognition system. The results clearly show the potential of the combination of morphable models and component-based recognition towards pose and illumination invariant face recognition.