Enhancing IoT-Based Smart Home Security Through a Combination of Deep Learning and Self-Attention Mechanism

Abstract

Deep learning and the Internet of Things (IoT) are rapidly advancing technologies with wide-ranging applications, notably in areas such as face detection. There is a growing need for sophisticated touchless authentication systems within this domain. While numerous security methods are available, many of them suffer from various shortcomings, including issues like forgotten passwords and the potential transmission of diseases through touch-based authentication techniques. This paper proposes IoT-based intelligent control face detection systems that leverage deep learning (DL) models. These systems are designed to significantly enhance security without relying on manually crafted rules. Face detection plays a pivotal role in safeguarding society, identifying wrongdoers, and bolstering community safety. However, challenges persist, particularly in achieving high accuracy with face recognition control systems in uncontrolled environments and real-time scenarios, such as intersections. In response to these challenges, our paper introduces a novel model that harnesses a combination of deep convolutional neural networks (CNNs) with self-attention mechanisms. Our experimental results demonstrate that this model can rapidly detect whole-scale images in a single forward pass. Notably, our proposed method achieved an outstanding accuracy rate of 99.7%. In comparison to existing state-of-the-art methods, our approach exhibits superior efficiency. This work showcases how the integration of IoT and DL, particularly with the use of a CNN with self-attention, outperforms other CNN-based approaches in terms of both speed and accuracy.