Design of a Face Recognition Technique Based MTCNN and ArcFace

The current trend of automation and data sharing in manufacturing technologies and daily living is the 4th Industrial Revolution. Computer vision technology has permeated our daily lives as a result of advancements in artificial intelligence and processing capacity. We propose using the ArcFace model, which blends deep neural networks with multi-tasking convolutional neural networks (MTCNN). The coding procedure of the agglomeration neural network facilitates the dimension-appropriate encoding of images. Techniques aimed at enhancing face recognition’s most distinctive characteristics. For the face recognition model to operate at peak efficiency, the facial recognition feature must integrate with finger gestures to control smart home activities, communicate with data, and link effortlessly to smart devices via IoT technology. We construct a facial recognition model utilizing an embedded Jetson Nano computer, a fingerprint scanning module, and a Raspberry Pi camera. The IoT smart home utilizes an embedded Raspberry Pi 3B + computer. The results indicate an approximate precision of 96% and a processing speed of 16 FPS. The interface of an Internet of Things (IoT) smart house illustrates the successful execution of real-time functionalities.