Enhanced Face Recognition Performance Through Convolutional Neural Networks

In recent days, Convolutional Neural Networks (CNNs) has demonstrated significant efficacy in the realm of facial recognition owing to their adeptness in extracting discerning features. This study introduces a facial recognition frame work employing a face identification and pre processing using Multi-Task Convolutional Neural Networks (MTCNN).Subsequently, this work employs the devised CNN model and a softmax function used for classification purposes. Experimental validation is conducted on the Olivetti Research Laboratory facial database. Results from our implementation showcase superior performance compared to prevalent models, achieving an accuracy rate above 95%.