Face recognition (FR) remains a critical component in security and authentication applications, requiring models that balance accuracy and computational efficiency. Traditional FR models rely on MLP-based classification heads, which serve as the standard approach for feature representation, but it remains an open research question as to which variant offers the optimal balance between model capacity and feature discriminability. In this paper, we introduce KANFace, a novel framework that integrates Kolmogorov-Arnold Networks (KANs) into face recognition architectures by replacing the conventional MLP-based head with a KANs module driven by adaptive, learnable B-spline activations. Evaluated across diverse datasets, including LFW, CFP-FP, and IJB-C, KANFace achieves state-of-the-art (SOTA) performance on CFP-FP, surpassing the baseline by 3% on pose-variant tasks, while maintaining comparable accuracy across other benchmarks. This work establishes a new paradigm in FR model design, demonstrating how KAN-based heads can enhance discriminative power and robustness in challenging face recognition scenarios. The KANFace code is available on our public repository at