This paper presents an approach for color face image denoising based on noisy pixel point detection derived from face image databases using the Chebyshev Inequality and neighborhood weight interpolation. The first step is principle component analysis (PCA) in the L^*a^*b^* color space for the face reconstruction. Then, a pixel vector distance variance and a pixel vector angle variance are defined and a noisy pixel detection strategy is implemented. Finally, an eight-neighborhood weight interpolation model is used to get a PCA recovered image for the final denoised color face image. The algorithm performance is verified with Gaussian noise, pepper-and-salt noise and block noise added into the PIE standard color face database. The method is compared with several state-of-the-art image denoising methods. Tests based on the PIE database show that this approach is effective with different kinds of noise and achieves better performance with clearer edges, more useful information, better visual effects and higher PSNR.