In order to solve the problem of low face recognition rate caused by the complex lighting conditions in coal mines, a face recognition method applied to the underground coal mines with complex lighting conditions is proposed. First, the face image is decomposed into low-frequency and high-frequency components by wavelet decomposition, and simultaneously the histogram equalization processing is conducted on the low-frequency components to enhance the image contrast. Then, the wavelet denoising model with a fuzzy degree of membership factor is used to filter the high-frequency components and meanwhile a new PAL fuzzy enhancement algorithm is adopted for the fuzzy enhancement of high-frequency components. Under different thresholds, a non-linear transformation is used to get feature images with different scales and different directions, and the anti-fuzzy processing is conducted. Finally, the processed low-frequency and high-frequency components are reconstructed based on wavelets. The experimental results show that the proposed face recognition method can be used to effectively improve the overall effect of face images and enhance the detail information of images under the complex lighting conditions in underground coal mines. Moreover, the average recognition rate can reach 94.45%, indicating the face recognition rate under complex lighting conditions in coal mines is significantly enhanced.