article
High-accuracy, real-time face detection is a critical requirement for enhancing cybersecurity across various domains. This paper proposes a novel approach for security augmentation through a hybrid hypermodel that synergistically combines DenseNet-121 and ResNet-50 architectures. Leveraging ResNet-50's residual connections for stable gradient flow and DenseNet-121's dense connectivity for efficient feature reuse, this model is designed to overcome limitations of standalone networks. The hypermodel is trained on diverse face datasets, including data augmented with varying brightness conditions to simulate real-world scenarios. Through real-time evaluation with OpenCV, the system achieves a high accuracy of 96 %, demonstrating its practicality and efficiency for security deployment. This research contributes a stable and highly effective face detection system, capable of accurately identifying individuals across diverse environments, thereby significantly enhancing security operations.
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DOI: 10.1109/itc-egypt66095.2025.11186582
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