Authors : Thella Preethi Priyanka; Teluguntla Siddhartha; Dhaniyala Sai Jaswanth; Avutapalli Prudhvi Krisha; Divi Taraka Durga Balaji

Volume/Issue : Volume 10 - 2025, Issue 4 - April

Google Scholar : https://tinyurl.com/yvpedrfs

Scribd : https://tinyurl.com/2xzjp27v

DOI : https://doi.org/10.38124/ijisrt/25apr783

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : This article provides the reader a tour of the most powerful face recognition systems available today, driven by Convolutional Neural Networks (CNNs). In our work, we dive deeply into the complexity of CNN models, going beyond surface study, to methodically create architectures that represent the greatest criteria of accuracy, durability, and efficacy in face recognition and classification. Additionally, we concentrate on the critical feature of resilience, carefully investigating alternative image preparation strategies, increasing model topologies, and measuring performance metrics. This extensive examination is not merely theoretical; rather, it is based on real applications, notably in the domains of computer vision and biometric identification. The purpose of this project is to develop face recognition technology by integrating creative approaches, subtle ideas, and real-world validations. Our objective is to expedite key security paradigm breakthroughs that will eventually lead to a more trustworthy, efficient, and secure environment for modern authentication systems.

Keywords : Face Recognition; Convolutional Neural Networks; Image Preprocessing; Model Training; Evaluation Metrics; Biometric Authentication; Computer Vision; Deep Learning Architectures; Transfer Learning Techniques; Feature Extraction Methods; Hyper Parameter Optimization; Adversarial Attacks Mitigation; Explainable AI in Face Recognition; Multimodal Biometric Fusion; Edge Computing for Real-time Recognition; Privacy-preserving Biometric Systems; Ethical Considerations in Authentication Systems.

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