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Gabor wavelet associative memory for face recognition.

Haihong Zhang·Bailing Zhang·Weimin Huang·Qi Tian
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Abstract

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.

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AlgorithmsArtificial IntelligenceCluster AnalysisComputing MethodologiesFaceHumansInformation Storage and RetrievalMemoryNeural Networks, ComputerPattern Recognition, AutomatedPhotographyComparative StudyEvaluation StudyJournal ArticleValidation Study

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Haihong Zhang, Bailing Zhang, Weimin Huang, Qi Tian. 2005. Gabor wavelet associative memory for face recognition.. https://doi.org/10.1109/tnn.2004.841811

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