Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Automatic Interpretation and Coding of Face Images Using Flexible Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Face Recognition by Elastic Bunch Graph Matching
IEEE Transactions on Pattern Analysis and Machine Intelligence
The FERET Evaluation Methodology for Face-Recognition Algorithms
IEEE Transactions on Pattern Analysis and Machine Intelligence
Handbook of AI
Distortion Invariant Object Recognition in the Dynamic Link Architecture
IEEE Transactions on Computers
Face Recognition through Geometrical Features
ECCV '92 Proceedings of the Second European Conference on Computer Vision
Face Recognition Based on Efficient Facial Scale Estimation
AMDO '02 Proceedings of the Second International Workshop on Articulated Motion and Deformable Objects
Neural Network-Based Face Detection
CVPR '96 Proceedings of the 1996 Conference on Computer Vision and Pattern Recognition (CVPR '96)
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
ICCHP '08 Proceedings of the 11th international conference on Computers Helping People with Special Needs
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The face localization is the most important process of automatic facial recognition. If facial position is estimated by using a geometrical model, the estimation accuracy deteriorates unless the model is optimal for facial appearance. In this work, we deal with variations of facial scale and individuals, and we propose an efficient method that estimates facial position in parallel with facial scale and a personal model. Facial position and scale are iteratively estimated by the process based on the dynamic link architecture and the scale conversion. The proposed method estimates the personal model by using a hierarchical face model database. As facial position estimation proceeds, the method selects the optimal model according to the hierarchical structure of the database. We can regard the selected personal model as a person identification result. We implement this facial recognition system based on the proposed method and demonstrate the advantages of the system through some experiments.