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IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Line-Based Face Recognition under Varying Pose
IEEE Transactions on Pattern Analysis and Machine Intelligence
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Digital Image Processing
Combination of Face Classifiers for Person Identification
ICPR '96 Proceedings of the International Conference on Pattern Recognition (ICPR '96) Volume III-Volume 7276 - Volume 7276
Automatic facial feature extraction by genetic algorithms
IEEE Transactions on Image Processing
Face recognition using kernel direct discriminant analysis algorithms
IEEE Transactions on Neural Networks
Face recognition using LDA-based algorithms
IEEE Transactions on Neural Networks
Gabor wavelet associative memory for face recognition
IEEE Transactions on Neural Networks
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This paper describes a method of hybrid classifier/recogniser based on Neuro-Genetic processing of face images. The use of Data Mining techniques has a legitimate and enabling ways to explore large image collections using the Neuro-Genetic approaches. Much research in human face recognition involves fronto-parallel face images, which are operated under strict imaging conditions such as controlled illumination and limited facial expressions. A novel Symmetric-Based Algorithm is proposed for face detection in still grey-level images, which acts as a selective attentional mechanism. A fusion of three face classifiers, Linear Discriminant Analysis (LDA), Line-Based Algorithm (LBA) and Kernel Direct Discriminant Analysis (KDDA), is proposed with Genetic Algorithm, which optimises the weights of neural network. It helps to extract only the essential features that effectively and successively improve the classification accuracy. The BioID face database, from BioID Laboratory, Texas, USA, has 1024 images for 22 subjects are used for analysis.