Predictive and Comparative Analysis of LENET, ALEXNET and VGG-16 Network Architecture in Smart Behavior Monitoring

Abstract

Along with the growth of IoT, ambient data and massive smart IOT devices, including several from cell phones and wearable electronics to robotic, self-driving taxis and remote - controlled objects, are changing how people go about their daily lives. The widespread use of IoT tools in services that are displayed with the user’s permission is critical to ensuring that customers have the lawful authority to use IoT strategies and tools and to preventing the destructive harm caused by a single hit from occurring in the localised liable areas. When consumers switch, consumer authentication is advantageous for inactive and tailored aids. For instance, when a family shares an independent car, the forceful patterns of family appendages change dramatically. Various assistance strategies contingent upon user identities maybe used to aid drivers. In an appropriate, user confirmation can help secure delicate data from high-tech-attacks. In this paper, the face data is used to identify people, and eye blink characteristics is used to track their behaviour. For this purpose, the LENET, ALEXNET, and VGGNET network architecture is used in the smart behaviour biometrics system and the comparative analysis using these network architectures for continuous authentication is done.