The face recognition process is used to distinguish individual’s faces based on their unique facial traits. In face recognition, the detection of faces in real-time videos under varying illumination conditions is one of the challenging tasks. In this study, we are detecting faces using Haar classifiers because of their high detection accuracy and local binary pattern (LBP) classifiers due to their invariant nature under varying illumination conditions. Image processing techniques such as contrast adjustment, bilateral filtering, histogram equalization, image blending, and quantization are applied to improve the detected faces. Also, we have applied quantization on raw face images at various levels to evaluate the feasibility of the proposed method in effectively recognizing the faces in low-quality images. Using local binary pattern histogram (LBPH) recognizer, a face recognition rate of 100% has been achieved when resized raw images and preprocessed images are blended. Also, an equal performance has been achieved when the quality of the images is reduced by applying quantization of 16 levels. Hence, the proposed method has proven its effectiveness in recognizing the faces in low-quality images. The results show that using the preprocessed image, the proposed face recognition method is invariant to varying illumination conditions.