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Adaptive Deep Metric Learning for Identity-Aware Facial Expression Recognition

机译：身份感知面部表情识别的自适应深度度量学习

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A key challenge of facial expression recognition (FER) is to develop effective representations to balance the complex distribution of intra- and inter- class variations. The latest deep convolutional networks proposed for FER are trained by penalizing the misclassification of images via the softmax loss. In this paper, we show that better FER performance can be achieved by combining the deep metric loss and softmax loss in a unified two fully connected layer branches framework via joint optimization. A generalized adaptive (N+M)-tuplet clusters loss function together with the identity-aware hard-negative mining and online positive mining scheme are proposed for identity-invariant FER. It reduces the computational burden of deep metric learning, and alleviates the difficulty of threshold validation and anchor selection. Extensive evaluations demonstrate that our method outperforms many state-of-art approaches on the posed as well as spontaneous facial expression databases.
机译：面部表情识别（FER）的关键挑战是制定有效的陈述，以平衡内部和阶级变异的复杂分布。通过Softmax损失惩罚图像的错误分类，提出了用于FER的最新卷积网络。在本文中，我们表明，通过联合优化将统一的两个完全连接的层分支框架中的深度度量损失和软MAX丢失相结合，可以实现更好的FER性能。为身份不变的FER提出了与身份感知硬负挖掘和在线正挖掘方案的广义式自适应（N + M）-Tuplet簇丢失函数。它降低了深度度量学习的计算负担，并减轻了阈值验证和锚选择的难度。广泛的评估表明，我们的方法优于提出的许多最先进的方法以及自发的面部表情数据库。

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