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Eyes opening and closing status is one of the most important components to monitor the driver's fatigue.d The current research mainly considers eyes blink frequency and the closing duration to judge the driver's fatigue. To identify driver's fatigue level, eyes opening and closing ratio (EOCR) is a critical factor and therefore, it is desirable to detect EOCR for driver's fatigue monitoring. The proposed method aims to simultaneously segment images and measure the parameters of the EOCR. A BiSeNet‐based iris and pupil segmentation network is first proposed and the Visual Geometry Group (VGG) ConvNet‐based model to detect the EOCR value is provided by considering the main features rounding eyes area and the iris‐pupil size for building test dataset. The comparison experiments are conducted with the proposed method and the other existing work in different datasets, such as CASIA‐Iris‐Thousand, CASIA‐Iris‐Interval, and UBIRIS.v2. The results demonstrate that the proposed method has superior detection effects on both infrared images and colour images than other existing approaches. Furthermore, the experiments of detecting the EOCR and iris and pupil segmentation are carried out with the test dataset and the results show that the proposed method can reliably identify driver's eye opening and closing degree.