MobileNet-Powered Deep Learning for Efficient Face Classification

Facial image analysis and categorization have recently made great strides in computer vision. The current study, explores ways to help computers better recognize faces quickly and accurately, especially for tasks like security and entertainment. Identifying faces, emotions, and identities is crucial in Security and Surveillance, Access Control, user Authentication in Smart Devices, and Emotion Analysis in Human-Computer Interaction. Adopting the MobileNet deep learning model because it requires less memory works efficiently. To make it even more effective at recognizing faces, adjusted its parameters and tested it with two data sets, the CASIA 3D face data set and 105 pins data set. The study using MobileNetV2 achieved a very high accuracy of 98.71% on the CASIA 3D face data set and 99.29% on the 105 pins data set. The experimental results show that MobileNetV2 better understands faces in different situations.