Existing three-dimensional (3D) face reconstruction methods struggle to capture high-frequency facial details, such as subtle expressions and fine skin textures, essential for accurate reconstruction and realistic user interaction. To address this limitation, we propose the implicit representation fusion network (IRFNet), a novel framework for precise facial geometry reconstruction. IRFNet integrates deformation-aware feature extraction and semantic facial segmentation, effectively combining local and global structural cues to optimize facial geometry accuracy. Additionally, a hybrid feature rendering mechanism enhances reconstruction consistency, particularly in complex environments. Compared to current approaches, IRFNet mitigates the geometric distortions inherent in explicit representations and better adapts to diverse facial morphologies and expression variations. Extensive experiments on real-world facial benchmarks demonstrate that IRFNet achieves state-of-the-art performance in 3D face reconstruction.