Interpersonal relation defines the association, e.g., warm, friendliness, and dominance, between two or more people. We investigate if such fine-grained and high-level relation traits can be characterized and quantified from face images in the wild. We address this challenging problem by first studying a deep network architecture for robust recognition of facial expressions. Unlike existing models that typically learn from facial expression labels alone, we devise an effective multitask network that is capable of learning from rich auxiliary attributes such as gender, age, and head pose, beyond just facial expression data. While conventional supervised training requires datasets with complete labels (e.g., all samples must be labeled with gender, age, and expression), we show that this requirement can be relaxed via a novel attribute propagation method. The approach further allows us to leverage the inherent correspondences between heterogeneous attribute sources despite the disparate distributions of different datasets. With the network we demonstrate state-of-the-art results on existing facial expression recognition benchmarks. To predict inter-personal relation, we use the expression recognition network as branches for a Siamese model. Extensive experiments show that our model is capable of mining mutual context of faces for accurate fine-grained interpersonal prediction.

中文翻译：

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从面部表情识别到人际关系预测

人际关系定义了两个或更多人之间的联系，例如热情、友好和支配。我们调查是否可以从野外的人脸图像中表征和量化这种细粒度和高层次的关系特征。我们通过首先研究用于面部表情鲁棒识别的深度网络架构来解决这个具有挑战性的问题。与通常仅从面部表情标签学习的现有模型不同，我们设计了一个有效的多任务网络，该网络能够从丰富的辅助属性（例如性别、年龄和头部姿势）中学习，而不仅仅是面部表情数据。而传统的监督训练需要具有完整标签的数据集（例如，所有样本都必须标有性别、年龄和表情），我们表明可以通过一种新的属性传播方法放宽这一要求。尽管不同数据集的分布不同，该方法还允许我们利用异构属性源之间的固有对应关系。通过该网络，我们在现有的面部表情识别基准上展示了最先进的结果。为了预测人际关系，我们使用表情识别网络作为连体模型的分支。大量实验表明，我们的模型能够挖掘人脸的相互上下文，以进行准确的细粒度人际预测。通过该网络，我们在现有的面部表情识别基准上展示了最先进的结果。为了预测人际关系，我们使用表情识别网络作为连体模型的分支。大量实验表明，我们的模型能够挖掘人脸的相互上下文，以进行准确的细粒度人际预测。通过该网络，我们在现有的面部表情识别基准上展示了最先进的结果。为了预测人际关系，我们使用表情识别网络作为连体模型的分支。大量实验表明，我们的模型能够挖掘人脸的相互上下文，以进行准确的细粒度人际预测。