Smart office automation via faster R-CNN based face recognition and internet of things

Abstract

Many constraints limit the accuracy level of classification of a face recognition system in smart office automation application, and these limitations make mask face recognition an important research area. In this research, a novel deep learning based Faster R-CNN which integrates with Internet of Things (IoT) to overcome the security issues in the office. The images of existing employees were gathered in a database and these images are pre-processed to train the neural network. Faster R-CNN employs VGG-16 as the foundation of its architecture to extract the features from pre-processed pictures. The recent development in Internet of Things (IoT) and deep learning have made it possible to addressing the difficulties of face recognition with deep neural network. Based on the feature classification, when a member of an organization approaches the door, it instantly opens. The door remains locked if it is an unknown individual. The images of a both authorized and unauthorized person were stored in a cloud and send it to the office manager for monitoring. The proposed Faster R-CNN model attain the accuracy range 99.3% better than the existing system. The proposed Faster R-CNN improves the overall accuracy ranges of 2.06%, 5.63%, 9.36%, and 3.54% better than Deep CNN, SVM, LBPH, and OMTCNN respectively.

Keywords

• Smart office automation
• Face recognition
• Faster R-CNN
• Internet of things