Feature Extraction and Feature Selection are the most important steps that can affect the performance of a Face Recognition system. In this research we have used a meta-heuristic approach to solve the problem of face recognition. The proposed algorithm is applied to features extracted using discrete wavelets and feature selection is done using Artificial Bee Colony (ABC) optimization where evolution is driven by fitness function defined in terms of correlation value for pattern recognition. Finally Ant Colony optimization (ACO) technique is used for face recognition by measuring the distances between the selected features. Experimental results show that the algorithm was found to generate encouraging recognition results with the minimal set of selected features.