IoT-enabled Contactless Doorbell with Facial Recognition
Gimhan Rodrigo, Dimanthinie De Silva · 2023
The COVID-19 epidemic has altered lifestyles all across the globe, causing people to take additional safety precautions and make using a face mask a requirement. Face masks are becoming more popular, making it occasionally challenging for people to recognize other people. Children and the elderly in particular would have trouble identifying their masked guests, which poses a serious hazard because thieves or burglars would take advantage of the situation. In this study, a system was created using IoT and deep learning technologies that works as a unit to offer a contactless solution to the ongoing COVID-19 pandemic while also enabling home owners to keep track of their visitors and receive notifications when someone comes over. The contactless doorbell was created with the help of a Raspberry Pi and a modified ResNet-50 model using ArcFace loss as the feature extractor to efficiently extract visible features from a masked face and support very accurate recognition. Due to the lack of a real masked face dataset with sufficient data, this study used a data augmentation method to add masks to face images from a dataset. The model was able to achieve a recognition accuracy of 98.27% when evaluated using a masked LFW dataset. Furthermore, testing the face recognition model in real-time with limited users, each with and without a mask yielded an accuracy of 100% in unmasked facial recognition and 90% on masked facial recognition.