论文标题
最佳基于运输的身份匹配，以识别身份不变的面部表达识别
Optimal Transport-based Identity Matching for Identity-invariant Facial Expression Recognition
论文作者
论文摘要
身份不变的面部表达识别（FER）一直是具有挑战性的计算机视觉任务之一。由于常规的FER方案不能明确解决面部表情的跨性别变化，因此其神经网络模型仍然取决于面部认同。本文建议通过利用通过特定匹配过程探索的相似表达式来量化认同的变化。我们将身份匹配过程作为最佳传输（OT）问题提出。具体而言，要找到来自不同身份的一对相似表达式，我们将功能间相似性定义为运输成本。然后，通过sindhorn-knopp迭代执行最低运输成本的最佳身份匹配，以找到最低运输成本的最佳流量。提出的匹配方法不仅容易插入其他型号，而且只需要可接受的计算开销。广泛的模拟证明，与野生数据集中的亚军相比，所提出的FER方法将PCC/CCC性能提高了10 \％或更多。源代码和软件演示可从https://github.com/kdhht2334/elim_fer获得。
Identity-invariant facial expression recognition (FER) has been one of the challenging computer vision tasks. Since conventional FER schemes do not explicitly address the inter-identity variation of facial expressions, their neural network models still operate depending on facial identity. This paper proposes to quantify the inter-identity variation by utilizing pairs of similar expressions explored through a specific matching process. We formulate the identity matching process as an Optimal Transport (OT) problem. Specifically, to find pairs of similar expressions from different identities, we define the inter-feature similarity as a transportation cost. Then, optimal identity matching to find the optimal flow with minimum transportation cost is performed by Sinkhorn-Knopp iteration. The proposed matching method is not only easy to plug in to other models, but also requires only acceptable computational overhead. Extensive simulations prove that the proposed FER method improves the PCC/CCC performance by up to 10\% or more compared to the runner-up on wild datasets. The source code and software demo are available at https://github.com/kdhht2334/ELIM_FER.
