Efficient representation of range face images using vectorfaces

Abstract

Advancement in scientific representation should accelerate the processing of images if it is more relevant and worthy with the experiment. Scientific visualizing of data (here, face images) has an enormous impact on exploring detailed inner content of images. Hence, the quality of processing depends on the quantity and informative data that might be accumulated, preserved as well as visualized in a particular image. In this paper, authors have described a novel technique for representation of range face image by „Vectorfaces‟, which is proved to be more effective towards better recognition purpose in terms of recognition rate. Range face image is particularly important for 2D visual images for accomplishing depth data from 3D images. Other than an efficient representation of „Vectorfaces‟ images, authors have also emphasized its significance for selecting better features compared to conventional range images. The major goal of the present work reported in this article is to evaluate, visualize and compare the role of „Vectorfaces‟ over range face images. Change of tracks for different mathematical notations to visualize the images are noted. Moreover, Mean-Maximum curvature image pair is accumulated from range image as well as „Vectorfaces‟ for extraction of features. SVD, followed by a feed-forward backpropagation neural network have been used for recognition purpose. In this work, 3D face images from Frav3D database have been considered. A statistical evaluation of this investigation is also given in the case study section.