Human face profile recognition by computer
Pattern Recognition
An Analytic-to-Holistic Approach for Face Recognition Based on a Single Frontal View
IEEE Transactions on Pattern Analysis and Machine Intelligence
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Face Recognition: Features Versus Templates
IEEE Transactions on Pattern Analysis and Machine Intelligence
Feature Space Transformation Using Genetic Algorithms
IEEE Intelligent Systems
Training Support Vector Machines: an Application to Face Detection
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Learning Support Vectors for Face Verification and Recognition
FG '00 Proceedings of the Fourth IEEE International Conference on Automatic Face and Gesture Recognition 2000
Face Recognition by Support Vector Machines
FG '00 Proceedings of the Fourth IEEE International Conference on Automatic Face and Gesture Recognition 2000
Gender Classification with Support Vector Machines
FG '00 Proceedings of the Fourth IEEE International Conference on Automatic Face and Gesture Recognition 2000
Support Vector Regression and Classification Based Multi-View Face Detection and Recognition
FG '00 Proceedings of the Fourth IEEE International Conference on Automatic Face and Gesture Recognition 2000
Visual Routine for Eye Detection Using Hybrid Genetic Architectures
ICPR '96 Proceedings of the International Conference on Pattern Recognition (ICPR '96) Volume III-Volume 7276 - Volume 7276
Face Pose Discrimination Using Support Vector Machines (SVM)
ICPR '98 Proceedings of the 14th International Conference on Pattern Recognition-Volume 1 - Volume 1
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Face recognition problem is challenging because face images can vary considerably in terms of facial expressions, 3D orientation, lighting conditions, hair styles, and so on. This paper proposes a method of face recognition by using support vector machines with the feature set extracted by genetic algorithms. By selecting the feature set that has superior performance in recognizing faces, the use of unnecessary information of the faces can be avoided and the memory requirement can be decreased significantly. Also, by using a tuning data set in the computation of the evaluation function, the feature set which is less dependent on illumination and expression can be selected. The experimental results show that the proposed method can provide superior performance than the previous method in terms of accuracy and memory requirement.