持有資料: Face recognition using integrated discrete cosine transform and kernel fisher discriminant analysis

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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Main Authors:	Janahiraman T.V., Omar J., Farukh H.N.
其他作者:	35198314400
格式:	Conference paper
出版:	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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Face recognition using integrated discrete cosine transform and kernel fisher discriminant analysis

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