<![CDATA[Facial recognition technology has become increasingly prevalent in modern applications, from security systems to social media platforms. However, one of the most significant challenges in this field remains the accurate detection of faces across varying scales, orientations, and image qualities. Traditional face detection methods often struggle when faces appear at different sizes within the same image or when dealing with low-resolution imagery, leading to inconsistent performance that can compromise system reliability. The RetinaFace architecture emerges as a promising solution to address these multiscale detection challenges. By incorporating a Feature Pyramid Network (FPN), the system creates a hierarchical representation of features that enables effective detection of faces regardless of their size in the image. The FPN combines low-resolution, semantically strong features with high-resolution, semantically weak features, creating a robust feature pyramid that simultaneously captures facial characteristics at multiple scales. Context modules within RetinaFace further enhance detection capabilities by providing additional contextual information that helps distinguish faces from background noise and other objects. This comprehensive approach allows the system to maintain high accuracy even in challenging scenarios where faces appear small, partially occluded, or at unusual angles. The comparative analysis between ArcFace and SphereFace loss functions reveals important insights into optimization strategies for facial recognition systems. The experimental results on the WIDERFACE dataset demonstrate exceptional performance, with the RetinaFace-ResNet152-SphereFace combination achieving 94% accuracy. These findings highlight the importance of architectural choices and loss function selection in developing robust facial recognition systems capable of handling real-world deployment challenges]]>
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