Facial recognition is a topic of interest for research as it has room for improvement in the accuracy of the recognition rate. To achieve this, either the recognition algorithm is modified or more efficient pre-processing techniques are used. This paper proposes a novel and optimized Artificial Bee Colony (ABC) algorithm, to perform facial recognition. Although the database being used here is Labeled Faces in the Wild (LFW), it is also tested on Carnigie Melon database to ensure consistent results. Applying the concept to perform facial recognition and obtaining satisfactory results is sublime. The discretization of the ABC algorithm serves many applications in the fields of pattern recognition and image analysis. This version of the ABC algorithm contains certain elements from Particle Swarm Optimization (PSO), hence yielding a hybrid algorithm that contains the best of both its contributors. This paper primarily focuses on the application of the proposed technique onto facial recognition. Standard data sets are used to test and quantify the efficacy of the algorithm. Given that the requirement is an extremely swift pattern recognition software that does not compromising the efficiency of the recognition rate, the proposed algorithm upholds both these criteria and is a robust technique.