Enhancing facial recognition accuracy through multi-scale feature fusion and spatial attention mechanisms

Abstract

Nowadays, advancements in facial recognition technology necessitate robust solutions to address challenges in real-world scenarios, including lighting variations and facial position discrepancies. We introduce a novel deep neural network framework that significantly enhances facial recognition accuracy through multi-scale feature fusion and spatial attention mechanisms. Leveraging techniques from FaceNet and incorporating atrous spatial pyramid pooling and squeeze-excitation modules, our approach achieves superior accuracy, surpassing 99% even under challenging conditions. Through meticulous experimentation and ablation studies, we demonstrate the efficacy of each component, highlighting notable improvements in noise resilience and recall rates. Moreover, the introduction of the Feature Generative Spatial Attention Adversarial Network (FFSSA-GAN) model further advances the field, exhibiting exceptional performance across various domains and datasets. Looking forward, our research emphasizes the importance of ethical considerations and transparent methodologies in facial recognition technology, paving the way for responsible deployment and widespread adoption in the security, healthcare, and retail industries.

Keywords

• facial recognition
• feature fusion
• spatial attention networks
• multi-scale feature extraction
• gan
• spoof detection