FACIAL RECOGNITION USING TRANSFER LEARNING IN THE DEEP CNN

Abstract

Facial recognition technology has been a popular research topic in recent years, and deep convolutional neural networks (CNNs) have shown impressive results in this field. However, the training of deep CNNs can be time-consuming and requires a large amount of labeled data. In this article, we explore the use of transfer learning techniques to improve the efficiency and accuracy of facial recognition tasks using deep CNNs. We investigate the effectiveness of fine-tuning pre-trained models and using feature extraction in transferring knowledge from a source domain to a target domain.