SMART VISION FACE ID DETECTION SYSTEM | IJCSE Volume 9 – Issue 6 | IJCSE-V9I6P7

Face recognition technology has become a crucial tool for contactless and efficient biometric authentication in devices and security systems. Despite its potential, existing models struggle in real- world conditions involving poor lighting, facial occlusions, and pose variations. Many are also vulnerable to spoofing attacks and face privacy concerns associated with centralized data storage. This paper presents the Smart Vision Face ID Detection System, an advanced deep learning-based framework designed to overcome these limitations. The system integrates facial recognition with age and gender prediction while incorporating liveness detection for real-human verification. It employs convolutional neural networks and transfer learning to enhance recognition accuracy and robustness under uncontrolled environments. Trained on diverse and balanced datasets, the system achieves high precision and reliable performance across various illumination, orientation, and background conditions. The inclusion of federated learning allows decentralized model training, ensuring data privacy and security. With a modular and lightweight architecture, it runs efficiently on resource-limited hardware. Experimental evaluation demonstrates superior accuracy, faster processing, and strong spoofing resistance compared to conventional methods. The Smart Vision Face ID Detection System provides a secure, adaptive, and scalable solution for access control in schools, workplaces, and public facilities. It also lays groundwork for future innovations in privacy-preserving and intelligent biometric systems.

The Smart Vision Face ID Detection System represents a significant step forward in biometric security technology by effectively combining state- of-the-art deep learning models with advanced privacy-preserving techniques. Its use of federated learning allows for decentralized training, keeping sensitive facial data secure on edge devices while maintaining model accuracy through collaborative updates. The system successfully manages to operate in diverse real-world conditions including variable lighting, facial occlusions, and distinct user demographics, demonstrating robustness and reliability. Additionally, the platform’s modular design supports the integration of supplementary features such as age and gender prediction, which not only enhance user profiling capabilities but also improve the system’s adaptability across sectors like healthcare, education, and workplace security. Its scalable cloud and edge- based infrastructure ensures low-latency responses suitable for real-time applications, complemented by comprehensive monitoring and security features. Overall, the Smart Vision Face ID Detection System offers a promising, efficient, and privacy-focused solution addressing the growing demands of modern digital identity verification.

[1]SMART VISION FACE ID DETECTION SYSTEM. International Journal of Novel Research and Development, 2025. Explores integration of AI, deep learning, and federated learning for privacy-preserving face recognition. [2]Face Recognition Systems: A Survey. PMC – PubMed Central, 2020. Comprehensive review of face recognition technologies highlighting deep learning methods. [3]Smart Identity Management System by Face Detection Using Multitasking Convolution Network. Wiley Online Library, 2021. Multi-task CNN approach for face detection with demographic prediction. [4]The application of federated learning in face recognition. EWADirect Proceedings, 2024. Discusses federated learning frameworks that preserve privacy and improve accuracy in face recognition. [5]AdaFedFR: Federated Face Recognition with Adaptive Inter-Class Representation Learning. arXiv, 2024. Proposes an efficient federated learning architecture for personalized face recognition. [6]Deep Learning-Based Prediction of Age and Gender from Facial Images. International Journal of Intelligent Engineering and Systems, 2023. Describes CNN models for robust demographic classification. [7]Federated Learning for Face Recognition via Intra- subject Self-supervised Learning. BMVC 2024. Presents a federated learning framework combining self-supervised learning for face recognition. [8]Human Age and Gender Prediction from Facial Images Using Deep CNN. ScienceDirect, 2024. Novel CNN-based models for real-world age and gender estimation. [9]A Comprehensive Review of Face Detection Using Deep Learning. All Research Journal, 2025. Survey of face detection advancements including CNN architectures. [10]Gender and Age Detection Using Deep Learning. IJRASET, 2025. Discusses the use of deep learning for facial attribute prediction in diverse datasets. [11]Prediction of the Age and Gender Based on Human Face Images Based on Deep Learning Algorithm. Wiley Online Library, 2022. Age and gender estimation with nutritional recommendation applications. [12]Vol. 44, No. 1, January 2025 Developing Deep Learning-based Facial Recognition Systems. Egyptian Journal, 2025. Covers advancements in facial recognition and demographic inference. [13]Machine Learning’s Role in Revolutionizing Face Recognition. Vertu AI Tools, 2025. Highlights breakthroughs in ML for real-time and accurate face recognition. [14]Design and Implementation of a Face Recognition System. ScienceDirect, 2021. Details the design of a face recognition framework including multi-task analysis. [15]The Advancements and Future Prospects of Federated Learning. SciTePress, 2024. Discusses federated learning applications in biometric and facial recognition systems.

Call for Paper (Hub) Fast Paper Publication IJCSE Indexing & Databases Submit Manuscript Call for Paper: Sep–Oct 2025 Call for Paper: Publish Research