Classification in data mining for face images using neuro:genetic approaches
International Journal of Artificial Intelligence and Soft Computing
Review of existing algorithms for face detection and recognition
CIMMACS'09 Proceedings of the 8th WSEAS International Conference on Computational intelligence, man-machine systems and cybernetics
Face recognition via direct search optimized Gabor filters
ICOSSE'06 Proceedings of the 5th WSEAS international conference on System science and simulation in engineering
Combining geometric and gabor features for face recognition
ACCV'06 Proceedings of the 7th Asian conference on Computer Vision - Volume Part I
Nonlinear quantization on Hebbian-type associative memories
Applied Intelligence
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This letter describes a high-performance face recognition system by combining two recently proposed neural network models, namely Gabor wavelet network (GWN) and kernel associative memory (KAM), into a unified structure called Gabor wavelet associative memory (GWAM). GWAM has superior representation capability inherited from GWN and consequently demonstrates a much better recognition performance than KAM. Extensive experiments have been conducted to evaluate a GWAM-based recognition scheme using three popular face databases, i.e., FERET database, Olivetti-Oracle Research Lab (ORL) database and AR face database. The experimental results consistently show our scheme's superiority and demonstrate its very high-performance comparing favorably to some recent face recognition methods, achieving 99.3% and 100% accuracy, respectively, on the former two databases, exhibiting very robust performance on the last database against varying illumination conditions.