Published Paper Details:

Humans excel at face recognition, the challenges of automatic facial recognition persist, particularly in scenarios involving aging, partial occlusion, and facial expressions. This paper proposes a novel face recognition framework comprising three key phases: Pre-Processing, Feature Extraction, and Classification. In the Pre-Processing phase, we enhance contrast and convert RGB images to Grey Level to improve the quality of facial images. Feature extraction involves capturing shape and texture using Active Appearance Models (AAM). The subsequent Classification phase employs an optimized LCDRC model. The pivotal component in the LCDRC model is the projection matrix, which plays a crucial role in enhancing recognition accuracy. To address this, we introduce a new hybrid algorithm known as the Combined Whale Lion Model (CWLM). The performance of our proposed model is evaluated based on metrics such as Accuracy, False Positive Rate (FPR), and False Discovery Rate (FDR). Comparative analysis is conducted against other methods, including LC-DRC, LCDRC-WOA, and LCDRC-CEWO.