A processor implemented method for adaptive illumination of an object, comprising: obtaining at least one image of an object, via one or more hardware processors, wherein the at least one image is captured when the object is illuminated by a plurality of Infrared (IR) illuminators from an IR illuminator array ; pre-processing the at least one image, via the one or more hardware processors, wherein the pre-processing comprises of a) adjusting contrast, and b) performing normalization, of one or more frames captured in the at least one image ; performing a semantic segmentation of pre-processed at least one image, via the one or more hardware processors, to extract foreground (FG) data and background (BG) data of the at least one image ; generating a binary semantic mask of the at least one image, via the one or more hardware processors, wherein the binary semantic mask comprises information on the extracted BG data and the FG data of the at least one image ; constructing an Edge based Structural Similarity Index Metric (ESSIM) matrix from the binary semantic mask, via the one or more hardware processors, wherein the ESSIM indicates a measured illuminance of each of a plurality of zones in at least one Region of Interest (RoI) of the object, in terms of a) luminance, b) contrast, and c) one or more edge comparison functions ; determining, via the one or more hardware processors, whether the measured illuminance of each of the plurality of zones of the object at least matches a threshold of illuminance ; and varying, via the one or more hardware processors, intensity of one or more of the plurality of illuminators, to improve the illuminance of each of the plurality of zones for which the measured illuminance is below the threshold of illuminance, to at least match the threshold of illuminance . obtaining at least one image of an object, via one or more hardware processors, wherein the at least one image is captured when the object is illuminated by a plurality of Infrared (IR) illuminators from an IR illuminator array ; pre-processing the at least one image, via the one or more hardware processors, wherein the pre-processing comprises of a) adjusting contrast, and b) performing normalization, of one or more frames captured in the at least one image ; performing a semantic segmentation of pre-processed at least one image, via the one or more hardware processors, to extract foreground (FG) data and background (BG) data of the at least one image ; generating a binary semantic mask of the at least one image, via the one or more hardware processors, wherein the binary semantic mask comprises information on the extracted BG data and the FG data of the at least one image ; constructing an Edge based Structural Similarity Index Metric (ESSIM) matrix from the binary semantic mask, via the one or more hardware processors, wherein the ESSIM indicates a measured illuminance of each of a plurality of zones in at least one Region of Interest (RoI) of the object, in terms of a) luminance, b) contrast, and c) one or more edge comparison functions ; determining, via the one or more hardware processors, whether the measured illuminance of each of the plurality of zones of the object at least matches a threshold of illuminance ; and varying, via the one or more hardware processors, intensity of one or more of the plurality of illuminators, to improve the illuminance of each of the plurality of zones for which the measured illuminance is below the threshold of illuminance, to at least match the threshold of illuminance . <EOS>