Face recognition has reached a high accuracy in recent years by adopting convolutional neural networks. However, it suffers from presentation attacks such as 2D face photos, and 3D masks. The vulnerability of face recognition and presentation attacks detection (PAD) attract numerous researchers in recent years. Most studies have only focused on PAD algorithms by analyzing texture information, depth information or thermal images. On the other hand, hyperspectral reflectance, which benefits from the development of line-scan HSI sensors, makes it possible to detect information about the inner structure of materials. Our research proposes an auxiliary method to support face recognition by analyzing hyperspectral reflectance. Combined with biological facts of human skin, we trained a neural network with pigmentation fractions inside human skin and corresponding reflectance. The results show high accuracy in identifying skin and non-skin.