Various investigations have shown that driver fatigue is the main cause of traffic accidents. Research on the use of computer vision techniques to detect signs of fatigue from facial actions, such as yawning, has demonstrated good potential. However, accurate and robust detection of yawning is difficult because of the complicated facial actions and expressions of drivers in the real driving environment. Several facial actions and expressions have the same mouth deformation as yawning. Thus, a novel approach to detecting yawning based on subtle facial action recognition is proposed in this study to alleviate the abovementioned problems. A 3D deep learning network with a low time sampling characteristic is proposed for subtle facial action recognition. This network uses 3D convolutional and bidirectional long short-term memory networks for spatiotemporal feature extraction and adopts SoftMax for classification. A keyframe selection algorithm is designed to select the most representative frame sequence from subtle facial actions. This algorithm rapidly eliminates redundant frames using image histograms with low computation cost and detects outliers by median absolute deviation. A series of experiments are also conducted on YawDD benchmark and self-collected datasets. Compared with several state-of-the-art methods, the proposed method has high yawning detection rates and can effectively distinguish yawning from similar facial actions.