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Compressed Sensing (CS) and Sparse Representation (SR) influenced the ways of signals are processed half a decade. The elegant solution to sparse signal recovery problem has found ground in several research fields such as machine learning and pattern recognition. The use of sparse representation and the solution of equations using ℓ1 minimization were utilized for face recognition problem under varying illumination and occlusion. Afterwards the idea was applied in biometrics to classify iris data. Similar to those studies, we use the discriminating nature of sparsity for the signals acquired in various signal domains and apply them to gesture recognition problem. The proposed algorithm in this context gives accurate recognition results over a recognition rate of 99% for user independent and 100% for user dependent gesture sets for fairly rich gesture dictionaries.