Integrating image processing techniques for enhancing facial recognition security in IoT devices

Access control in IoT-based systems mostly depends on face recognition, but it still faces many issues. Issues as face recognition errors, facial feature changes, and security problems like spoofing attempts. The primary focus in this work was improving the security of the system along with its reliability by managing advanced image processing techniques, such as depth analysis, liveness detection, and recurring authentication updates To make the system more restricted to fraudulent access and optimized at managing crucial cases, these additions are made, which are not just some technical updates but they also play an important part. Furthermore other methods like anomaly detection, and feature extraction were used to enhance the robustness of system, while allowing it to adjust to new issues effectively. Issues relevant to algorithmic bias and user privacy have also been taken into consideration while working on this study, by evaluating that not just system is secure but also it's not biased and is ethical. Combination of work have been suggested in the results, where the system is security-focused and ethically aware design makes it more effective and reliable facial recognition system, with strong potential for future use in IoT-based authentication environments.