In an unconstrained environment, face detection is challenging due to differences in light, occlusion, and expressions. The accuracy of the multi-task cascaded convolutional neural network ( MTCNN) face detector is reduced in low-light environments. To improve the accuracy of face detection in low-light environment, a MSRCR-based optical frequency segmented filtering enhancement algorithm ( 3CGF-MSRCR) is proposed. This paper uses MTCNN for face detection, and uses a RGB three-channel decomposition guided filtering (GF) method to improve the multi-scale retinex with color restoration. Firstly, face images are enhanced by MSRCR and decomposed into RGB three channels to obtain the image weights of RGB. Then the GF method is used to filter each channel separately and update the weights of the RGB images. Finally, we reconstruct the face image. Training and testing are conducted on the actual lowlight scene face dataset: Dark Face and the public standard face dataset CelebA. Meanwhile, the running time of the proposed algorithm is compared with other enhancement algorithm. The results show that 3CGF-MSRCR can effectively suppress the high-frequency noise of MSRCR, retain the brightness enhancement effect, and improve the accuracy. Meanwhile, 3CGF-MSRCR has a faster running speed.