One-class Selective Transfer Machine for Personalized Anomalous Facial Expression Detection
One-class Selective Transfer Machine for Personalized Anomalous Facial Expression Detection
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DOI：
10.5220/0006613502740283
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发表时间：
2018
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影响因子：
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通讯作者：
Hirofumi Fujita;Tetsu Matsukawa;Einoshin Suzuki
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文献类型：
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作者：
Hirofumi Fujita;Tetsu Matsukawa;Einoshin Suzuki
异常面部表情是日常生活中很少出现的面部表情，它暗示着异常的身体或精神状况。在本文中，我们提出了一种用于检测异常面部表情的一类迁移学习方法。在面部表情检测中，大多数文章提出了预测所有人样本类别的通用模型。然而，人们的面部形态存在差异，例如眉毛的粗细，这种个体差异常常会导致预测错误。虽然一种可能的解决方案是仅从目标人的样本中学习单任务分类器，但由于实际应用中目标人的样本量较小，它常常会过度拟合。为了处理异常检测中的个体差异，我们扩展了选择性转移机（STM）（Chu et al., 2013），它通过根据样本与目标样本的接近程度重新加权样本来学习个性化的多类分类器。与面部表情个性化模型的相关方法（包括STM）相比，我们的方法学习一类分类器，只需要一类目标和源样本，即正常样本，因此不需要收集很少出现的异常样本。在公共数据集上的实验表明，我们的方法优于使用单类 SVM 的通用模型和单任务模型，以及最先进的多任务学习方法。
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.