Abstract
A wide range of approaches for 2D face recognition (FR) systems can be found in the literature due to its high applicability and issues that need more investigation yet which include occlusion, variations in scale, facial expression, and illumination. Over the last years, a growing number of improved 2D FR systems using Swarm Intelligence and Evolutionary Computing algorithms have emerged. The present work brings an up-to-date Systematic Literature Review (SLR) concerning the use of Swarm Intelligence and Evolutionary Computation applied in 2D FR systems. Also, this review analyses and points out the key techniques and algorithms used and suggests some directions for future research.
Highlights
Face recognition (FR) systems are widely used in di erent parts of the society which includes, for example, residences, public places, industries, commercial shops and o ces
Among di erent kind of approaches proposed during the last decades, some studies can be found in literature which are focused on approaches that employ optimization techniques inspired by nature, i.e. bio-inspired optimization techniques (Bowyer et al.; 2006; Abate et al.; 2007; Islam et al.; 2012; Scheenstra et al.; 2005; Kong et al.; 2005; Zhao et al.; 2003; Alsalibi et al.; 2015)
Much interest and research have been focused on the eld of face recognition (FR) and an increased number of bio-inspired FR systems had been emerged for di erent purposes which include feature selection, parameters optimization, template matching and classi cation
Summary
Face recognition (FR) systems are widely used in di erent parts of the society which includes, for example, residences, public places, industries, commercial shops and o ces. Among many versions of PSO, its binary version has been widely used to nd the most discriminative set of features in facial images improving FR systems (Vora et al.; 2014; Varun et al.; 2015; Varadarajan et al.; 2015) Another popular SIbased algorithm is the Ant Colony Optimization (ACO) (Dorigo and Stutzle; 2003), which is inspired by the collective behavior of ants in nding the shortest path between the nest and the food source through a substance called pheromone. EC-based algorithms are inspired by the evolutionary theory proposed by Darwin In this branch we may cite the Genetic Algorithm (GA) (Holland; 1973), in which natural selection and genetic operators play the main role, and has been used for feature selection and classi cation in FR systems (Fan and Verma; 2004; Zheng et al.; 2005; Liu and Wang; 2006).
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