Current fatigue monitoring equipment is expensive, non-portable, and its algorithm has a low robustness in eye movement image processing. Existing front-end camera fatigue monitoring schemes have many limitations and greatly affect their users efficiency; as such, the development of portable and accurate fatigue monitoring systems is very challenging. In this paper, a new type of fatigue monitoring glasses and N-Range image processing algorithm is proposed to improve the robustness of eye location analysis and accuracy of fatigue detection. Real-time fatigue analysis is performed for users of the proposed mechanism according to the theory of percentage eye closure (PERCLOS) P80 fatigue assessment; it is considered that the P80 criterion is the most suitable for this study. In the process of side-eye image processing, an N-Range eye region extraction algorithm is proposed. The activation map is computed by an N*N convolution kernel, and it is segmented by the OTSU threshold segmentation method. The activation value of pixels smaller than the threshold is set at zero. Based on this, the standard deviation projection in the horizontal and vertical directions is calculated; the human eye region is located by the average threshold method from the projection map. The eye closure degree is measured by calculating the ratio of eye height to eye width and then counting the closure time. Experiments show that the aforementioned problems can be adequately solved by our method. Even in complex environments, the method in this paper still performs well, with fatigue judgment accuracy reaching 94%. The fatigue-monitoring scheme proposed in this paper, can be positively adapted under many uncertainties. As such, our fatigue monitoring glasses and N-Range algorithm can achieve high accuracy without affecting the efficiency of workers.