Boosting CNN network performance for face recognition in an authentication system

Face recognition technology has made significant advancements through the utilisation of convolutional neural networks (CNN) in various applications. However, accurately identifying individuals from similar backgrounds remains a notable challenge due to inherent similarities in facial features among individuals with shared genetic ancestry or cultural heritage. This paper addresses the limitations of traditional CNN in accurately identifying individuals from the same origins and presents an approach to enhance the performance of CNN networks and improve the reliability of face recognition in authentication systems. The proposed approach incorporates an advanced face detection and identification algorithm based on the visual geometry group face (VGG-Face) CNN descriptor model, along with the cosine distance algorithm. Promising results were obtained through a prototype implementation on a Raspberry Pi 4. Comparative evaluations against alternative face recognition strategies showcased exceptional performance, achieving an accuracy rate of 96.33% for positive pairs and 95.38% for negative pairs at an optimal threshold of 20.