Beyond the Mask: Advancements in Facial Recognition through Convolutional Neural Networks

Abstract

Abstract: Facial recognition technology has gained immense popularity in recent years, yet challenges persist with face alterations and the widespread use of masks. Uncooperative individuals in real-world scenarios, such as video surveillance, often resort to masking, leading to a degradation in current face recognition performance. While extensive research has addressed face recognition under various conditions, the impact of masks has often been overlooked. This research focuses specifically on improving the accuracy of recognizing faces obscured by different masks. Our proposed approach involves initial detection of facial regions, addressing the occluded face detection problem through the Multi-Task Cascaded Convolutional Neural Network (MTCNN). Subsequently, facial feature extraction utilizes the Google Face Net embedding model, followed by a classification task executed by Support Vector Machine (SVM). Experimental results demonstrate the effectiveness of this approach in masked face recognition, with noteworthy performance even under excessive facial masks. Additionally, a comparative study enhances our understanding of the proposed method's capabilities