Adversarial attacks pose a critical threat to deep face recognition systems by introducing subtle, often imperceptible perturbations that mislead model predictions. While extensive research has addressed attack efficacy and defense strategies, limited attention has been given to the spatial behavior of these perturbations—specifically, where they concentrate in facial images and how they affect structurally meaningful regions. This study presents a region-based analysis framework to examine the spatial localization, distribution, and perceptual impact of adversarial noise across edge-dense and smooth regions of facial images. Five adversarial attack methods (FGSM, PGD, CW, DeepFool, and GSM) and two state-of-the-art face recognition models (ResNet-50 and FaceNet) are employed in a comprehensive evaluation on the Labelled Faces in the Wild (LFW) dataset. The results indicate that adversarial perturbations disproportionately concentrate on edge-rich facial features such as the eyes, mouth, and jawline—areas critical for identity encoding—while maintaining high global perceptual similarity. Further analysis reveals that different attacks exhibit distinct spatial patterns: some distribute noise broadly across contours, while others target semantically dense regions with minimal perturbations. Varying the perturbation strength demonstrates a dual effect on noise visibility and localization, highlighting a trade-off between stealth and structural disruption. These insights provide a foundation for the development of spatially informed defenses and contribute to greater interpretability in adversarial robustness research for biometric systems.