Abstract
Nowadays, multimedia data on social media platforms of the Internet, especially images, are proliferating, due to which image retrieval systems based on content are one of the major research areas. Despite the introduction of various content-based images retrieval approaches, face recognition remains a problematic task. Consequently, this paper developed a face recognition framework using modifying the bag-of-features (BOF) from a grey wolf optimisation (GWO) algorithm in which a variable weight is used. Variable weight proposes a sine cos function to find the best score. The convergence behaviour of the variable weight GWO algorithm is performed over some benchmark functions. Furthermore, it has been used to get the histograms fed into training and testing function to get the model accuracy using the Yale face database.
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