Among various types of biometric techniques it is the face recognition that gains huge popularity in the last few decades due to its vast applicationsand more user friendly nature.Face Recognition is the procedure of identification and verification of a person's identity using his face. Main steps involved in a FR System are Face Detection, Feature Extraction, Feature Selection and Recognition. Feature Extraction and Feature Selection are the two main phases to be focused on in order to get a good FR System. In this paper, we have carried out anextensive literature review on face recognition focusing on feature extraction and feature selection phases a bit more.

The development of an effective and efficient face recognition system has always been a challenging task for researchers. In a face recognition system, feature selection is one of the most vital processes to achieve maximum accuracy by removing irrelevant and superfluous data. Many optimization techniques, such as particle swarm optimization (PSO), genetic algorithm (GA), ant colony optimization, etc., have been implemented in face recognition systems mainly based on two feature extraction methods: discrete cosine transform (DCT) and principal component analysis (PCA). In this research, a nature-inspired well-known algorithm, namely cuckoo search, has been implemented for face recognition. Further, a hybrid method consisting of DCT and PCA is applied to extract the various features by which recognition can be made with a high rate of accuracy. To validate the proposed methodology, the results are also compared with the existing methodologies, such as PSO, differential evolution, and GA.