1. An adaptive face recognition system, comprising: at least one electronic processor having a central processing unit; at least one database having a plurality of pixilated face images of known subjects of interest, wherein said at least one database is associated with said at least one electronic processor; at least one test image, j, of a new subject of interest, wherein said at least one test image is a pixilated face image configured for input into said at least one electronic processor; a classification processing tool associated with said at least one electronic processor, wherein said classification processing tool is configured to build a dictionary, A, associated with said at least one database, wherein said classification processing tool is configured to provide a classification match of said at least one test image, j, with one of said plurality of pixilated face images of known subjects of interest; and at least one device associated with said at least one electronic processor configured to output in a tangible medium said classification match; wherein said classification processing tool is a non-transitory electronic-processor-readable medium having a plurality of electronic processor executable instructions stored thereon, that when executed by said at least one electronic processor, causes said at least one electronic processor to: input said at least one test image, j, into said at least one electronic processor; perform adaptive high-pass filtering, HF, on said at least one test image, j, to increase the number of keypoints, KP, in said at least one test image, j, to reduce changes in illumination in said at least one test image, j, wherein said keypoints, KP, are structures of interest in said at least one test image, j, including eyes, brows, nose, mouth, and hairline features, wherein said adaptive high-pass filtering, HF, uses at least two high-pass filters having different spatial frequency bandwidths dependent on the pixel size of said at least one test image, j; compute at least one scale invariant feature transform (SIFT) descriptor, N, for each of said at least one test image, j; recognize said new subject of interest in said at least one test image, j, by using an optimization technique, l 1 , to search for a best match of said new subject of interest in said at least one test image, j, in said at least one database having a plurality of pixilated face images of known subjects of interest; determine whether to learn and alter the current knowledge of said dictionary, A from A c(j-1) to A c(j) ; output a class having maximum output, q max ; when it is determined to learn, adapt said dictionary, A, using reversed-orthogonal matching pursuit (OMP); and iterate through said recognize said new subject of interest in said at least one test image, j, by using an optimization technique, l 1 , to search for a best match of said new subject of interest in said at least one test image, j, in said at least one database having a plurality of pixilated face images of known subjects of interest task, said determine whether to learn and alter the current knowledge of said dictionary, A task, and said output a class having maximum output, q max , until it is determined not to learn.