Abstract: 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 problem is resolved through modified maximum margin criterion 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.

Illumination variation is a challenging problem in face recognition research area. Same person can appear greatly different under varying lighting conditions. This paper consists of Face Recognition System which is invariant to illumination variations. Face recognition system which uses Linear Discriminant Analysis (LDA) as feature extractor have Small Sample Size (SSS). It consists of

Face recognition is a computer based person identification technique based on arithmetical and numerical features obtained from face images. In a face recognition system, illumination has been a great problem. In this work, light variation is tried to dominate to increase recognition rate. To improve the face recognition rate further, a novel technique is adopted, where facial images are vertically divided into two equal halves and D2DPCA dimension reduction technique is applied in each halves. Normalization is performed by two different techniques, which are Min-Max and Z-Score recognition rates achieved by them are 95.76 % and 95.90 respectively. Comparing proposed technique with conventional technique, Hence it is shown that proposed technique works well for illumination variant images and D2DPCA is better dimension reduction tool than 2DPCA.