LBP and majority of its variants performs extremely well in front of moderatelight variations. But when light variations becomes severe then performanceof LBP and its variants is not satisfactory. Therefore there is a need of themore promising and impressive descriptor which performs well in harsh lightvariations. To complement these LBP based descriptors the proposed worklaunches the novel descriptor for Face Recognition (FR) in harsh lightningvariations. This proposed descriptor is called as Radial Orthogonal MedianLBP (ROM-LBP). The main demerit of these LBP based descriptors is thatthey all consider the uniform coordination between the neighbors and centerpixel. Which mean raw pixel intensity is used for the comparison with thecenter pixel. The proposed work eliminates this problem in the introduceddescriptor ROM-LBP, by replacing the raw pixels intensity with the medianof the radial points in each orthogonal position of the two separate groups.The generated median is then used for comparison with the center pixel.The respective codes obtained from both the groups are concatenated toform the ROM-LBP size. As region feature extraction is done therefore ROM-LBP develops the large feature size. To make more effective descriptor,the services of FLDA is used and then classification was conducted by SVMs.Experiments conducted on EYB and YB datasets demonstrates the ability ofthe proposed ROM-LBP against various LBP and non-LBP based descriptors.