A Comparative Analysis of Selected Fisher Linear Discriminant Based Algorithms in Human Faces
One of the most reliable biometrics when issues of access control and security is been considered is face recognition. An integral part of a face recognition system is the feature extraction stage, which becomes a critical problem where is a need to obtain the best feature with minimum classification error and low running time. Many of the existing face recognition systems have adopted different linear discriminant-based algorithms independently for feature extraction in which excellent performance were achieved, but identifying the best most suitable of these variants of linear discriminant-based algorithms for face recognition systems remains a subject open for research. Therefore, this paper carried out a comparative analysis of the performance of the basic Linear Discriminant Algorithm (LDA) and two of its variants which are Kernel Linear Discriminant Analysis (KLDA) and Multiclass Linear Discriminant Analysis (MLDA) in face recognition application for access control.
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