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Human-centric driver fatigue monitoring systems (DFMS) with integrated sensing, processing and networking aim to find solutions for traffic accidents and other relevant issues. A novel, efficient combined features extraction approach from Pyramid Histogram of Oriented Gradients (PHOG) and contourlet transform (CT) for fatigue expression descriptions of vehicle drivers is proposed, and a random subspace ensemble (RSE) of linear perception (LP) classifiers as the base classifier is then exploited for the classification of three predefined fatigue expressions classes, namely, awake expressions, moderate fatigue expressions and severe fatigue expressions. Holdout and cross-validation experiments are created, and the results show that combined features by RSE of LP classifiers outperform the other seven classifiers, that is, PHOG features by LP classifier, CT features by LP classifier and combined features by five individual LP classifiers. With combined features and RSE of LP classifiers, the average classification accuracies of three fatigue expression classes are over 92% in both the holdout and cross-validation experiments. Among the three fatigue expression classes, the class of severe fatigue expressions is the most difficult to recognise, and the classification accuracy is over 84% in both the holdout and cross-validation experiments, which shows the effectiveness of the proposed feature extraction method and RSE of LP classifiers in developing DFMS.