Face Recognition Using Adaptive Margin Fisher’s Criterion and Linear Discriminant Analysis (AMFC-LDA)

Selecting a low dimensional feature subspace from thousands of features is a key phenomenon for optimal classification. Linear Discriminant Analysis (LDA) is a basic well recognized supervised classifier that is effectively employed for classification. However, two problems arise in intra class during discriminant analysis. Firstly, in training phase the number of samples in intra class is smaller than the dimensionality of the sample which makes LDA unstable. The other is high computational cost due to redundant and irrelevant data points in intra class. An Adaptive Margin Fisher's Criterion Linear Discriminant Analysis (AMFC-LDA) is proposed that addresses these issues and overcomes the limitations of intra class problems. Small Sample Size (SSS) problem is resolved through modified Maximum Margin Criterion (MMC), which is a form of customized LDA and convex hull. Inter class is defined using LDA while intra class is formulated using quick hull respectively. Similarly, computational cost is reduced by reformulating within class scatter matrix through minimum Redundancy Maximum Relevance (mRMR) algorithm while preserving discriminant information. The proposed algorithm reveals encouraging performance. Finally, a comparison is made with existing approaches.