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Comparative study of face recognition techniques that use joint transform correlation and principal component analysis.

A Alsamman·Mohammad S Alam
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Abstract

Face recognition based on principal component analysis (PCA) that uses eigenfaces is popular in face recognition markets. We present a comparison between various optoelectronic face recognition techniques and a PCA-based technique for face recognition. Computer simulations are used to study the effectiveness of the PCA-based technique, especially for facial images with a high level of distortion. Results are then compared with various distortion-invariant optoelectronic face recognition algorithms such as synthetic discriminant functions (SDF), projection-slice SDF, optical-correlator-based neural networks, and pose-estimation-based correlation.

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AlgorithmsArtificial IntelligenceCluster AnalysisFaceHumansImage EnhancementImage Interpretation, Computer-AssistedInformation Storage and RetrievalLightNumerical Analysis, Computer-AssistedPattern Recognition, AutomatedPrincipal Component AnalysisReproducibility of ResultsSensitivity and SpecificitySignal Processing, Computer-AssistedStatistics as TopicUser-Computer InterfaceComparative StudyEvaluation StudyJournal Article

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A Alsamman, Mohammad S Alam. 2005-02-10. Comparative study of face recognition techniques that use joint transform correlation and principal component analysis.. https://doi.org/10.1364/ao.44.000688

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