Abstract
Face recognition is a method of biometric identification technology that uses a person's face characteristic data. Face-based characteristics can be easily acquired, unlike fingerprints, iris, and other biometrics, they can be collected without physical contact. Face recognition technology has therefore rapidly grown and is now widely employed in a variety of industries, including banking, manufacturing, banking, healthcare, and education. Convolutional neural networks (CNNs) have attained good results in face recognition with the constant developments in deep learning. However, throughout the training phase, deep convolution neural networks have challenges in convergence and optimization. These issues are resolved by residual networks. Furthermore, the channel attention techniques can support networks in learning only the characteristics that carry valuable information, hence enhancing the model’s accuracy. In this study, we first use the swish function to enhance the initial residual network to produce the improved residual network and then the spatial and channel attention mechanisms, are added to the Resnet. The experimental findings of face recognition on MegaFace, CFP, LFW, and AgeDB datasets demonstrate that our model performed significantly in various metrics like specificity, sensitivity, test accuracy, F1 score, Matthew's correlation coefficient, precision, and test loss.