An anomalous facial expression is a facial expression which scarcely occurs in daily life and coveys cues about an anomalous physical or mental condition. In this paper, we propose a one-class transfer learning method for detecting the anomalous facial expressions. In facial expression detection, most articles propose generic models which predict the classes of the samples for all persons. However, people vary in facial morphology, e.g., thick versus thin eyebrows, and such individual differences often cause prediction errors. While a possible solution would be to learn a single-task classifier from samples of the target person only, it will often overfit due to the small sample size of the target person in real applications. To handle individual differences in anomaly detection, we extend Selective Transfer Machine (STM) (Chu et al., 2013), which learns a personalized multi-class classifier by re-weighting samples based on their proximity to the target samples. In contrast to related methods for personalized models on facial expressions, including STM, our method learns a one-class classifier which requires only one-class target and source samples, i.e., normal samples, and thus there is no need to collect anomalous samples which scarcely occur. Experiments on a public dataset show that our method outperforms generic and single-task models using one-class SVM, and a state-of-the-art multi-task learning method.