CONTRASTIVE CONVOLUTION IN FACE RECOGNITION: ADVANCEMENTS IN ACCURACY

Abstract

The rapid advancements in face recognition have underscored the need for more precise and efficient methodologies. This article explores the integration of contrastive convolution within traditional convolutional neural networks (CNNs) as a promising avenue. Addressing challenges like pose variations, inconsistent lighting, and facial expression changes, the introduction of the FaceNet Convolution, powered by contrastive learning, offers a refined mechanism for facial recognition. The research highlights the significant improvements in accuracy through this integration, setting the stage for a future with more accurate and computationally efficient face recognition systems.