Ordinal matrix encoding based facial recognition
摘要
Now-a-days, ordinal patterns are shown to be effective in extracting discriminant image features. In this paper, we present the ordinal matrix encoding (OME) as a method that transforms an 8-bit encoded image into another image with s gray levels. Such an encoding acts as a highpass filter and allows us to enhance the image contours that are useful for feature extraction. In this work, we hybridized the OME technique with the linear discriminant analysis (LDA) approach to define the modified LDA (MLDA) to extract image features. The MLDA considers only interclass matrices of encoded images to highlight their singularities. Subsequently, a support vector machine (SVM) is applied to the MLDA output to perform facial image classification. We validated the proposed classification method using images from the ORL, FERET and FEI standard databases. The results indicate an overall accuracy of