A DenseUnet Generative Adversarial Network (GAN) is proposed to colorize near-infrared (NIR) face images. The GAN generator incorporates the advantages of both DenseNet and Unet structures. The DenseNet extracts facial features effectively by increasing the network depth, and the Unet keeps important facial details through skip-connection. The generator further integrates an optimized facial loss function designed by considering pixel loss, color loss and feature loss. The proposed network is evaluated against state-of-the-art grayscale image colorization GANs over two separate datasets. Both qualitative and quantitative comparisons demonstrate that with improved network structures and loss constrains the DenseUnet GAN can colorize NIR face images with natural color, minimal face shape distortion, and rich facial details.