Facial recognition systems are widely used in critical areas such as authentication and access control, but remain vulnerable to adversarial attacks that can severely compromise their reliability. We introduce a novel hybrid attack framework that combines geometric and intensity-based perturbations to effectively fool facial recognition models. Our approach begins with isoline detection to identify facial contours critical for identity discrimination. These contours are strategically perturbed using geometric transformations to disrupt structural features, while localized intensity-based adversarial masks are applied to high-saliency regions (e.g., eyes, mouth) to maximize feature confusion. Affecting only 10% of facial pixels, our method achieves an optimal balance between attack potency and imperceptibility. Evaluations on two benchmark datasets demonstrate that the proposed framework outperforms state-of-the-art adversarial techniques under black-box settings. These findings highlight critical vulnerabilities in current systems and underscore the urgent need for robust defense mechanisms against multifaceted adversarial strategies.