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Face recognition using integrated discrete cosine transform and kernel fisher discriminant analysis

In face recognition applications, the dimension of the sample space is usually larger than the number of the samples in a training set. As a result, Fisher Linear Discriminant Analysis (FLD) based methods suffers due to singularity problem (of scatter matrix). This situation is often referred as &qu...

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書誌詳細
主要な著者:	Janahiraman T.V., Omar J., Farukh H.N.
その他の著者:	35198314400
フォーマット:	Conference paper
出版事項:	2023
主題:	Computer science Cosine transforms Discrete cosine transforms Discriminant analysis Feature extraction Fisher information matrix Learning algorithms Nonlinear analysis Complex data Face images Fisher linear discriminant analysis High-dimensional Input sample Kernel fisher discriminant analysis Linear algorithms Nonlinear features Nonlinear kernel functions ORL database Recognition accuracy Sample space Scatter matrix Singularity problems Small Sample Size Training sets Face recognition
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