RISA: Rotation Illumination Scale and Affine Invariant Face Recognition

Abstract

Face Recognition (FR) has been on the forefront of research efforts for the past two decades. In spite of considerable strides, it still suffers from the curse of false matches in the presence of variations in terms of parameters such as affine, scale, rotation and illumination. Since, real world images inherently consists of such variations, an effective FR system, should handle such variations deftly. Hence, in this paper, we propose a robust, yet simple and cost effective technique for overcoming some of the aforementioned challenges. The first stage of the proposed system deals with illumination variations by performing logarithm transform on the input face images. Further, the Non-subsampled Contourlet Transform (NSCT) is used to decompose the logarithm transformed facial images into low frequency and high frequency components. Subsequently, histogram equalization is carried out on the low frequency components. Finally, we employ Affine Scale Invariant Feature Transform (ASIFT) to find corresponding points that are translation and scale invariant. We will demonstrate by carrying out extensive experimentations on the benchmark datasets: ORL, Grimace, Face95 and Yale, that the proposed technique is more robust and yields comparable efficacy to most of the contemporary approaches.