Heuristic approach for face recognition using artificial bee colony optimization

Abstract

Artificial Bee Colony (ABC) algorithm is inspired by the intelligent behavior of the bees to optimize their search for food resources. It is a lately developed algorithm in Swarm Intelligence (SI) that outperforms many of the established and widely used algorithms like Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) under SI. ABC is being applied in diverse areas to improve performance. Many hybrids of ABC have evolved over the years to overcome its weaknesses and better suit applications. In this paper ABC is being applied to the field of Face Recognition, which remains largely unexplored in context of ABC algorithm. The paper describes the challenges and methodology used to adapt ABC to Face Recognition. In this paper, features are extracted by first applying Gabor Filter. On the features obtained, PCA (Principal Component Analysis) is applied to reduce their dimensionality. A modified version of ABC is then used on the feature vectors to search for best match to test image in the given database.