论文标题
ACR损失：基于自适应坐标的回归损失面部对齐
ACR Loss: Adaptive Coordinate-based Regression Loss for Face Alignment
论文作者
论文摘要
尽管深度神经网络在解决面部对齐方面取得了合理的准确性，但它仍然是一项具有挑战性的任务，特别是当我们处理面部图像，闭塞或极端头部姿势时。基于热图的回归（HBR）和基于坐标的回归（CBR）是面部比对的两种主要使用方法之一。 CBR方法需要更少的计算机内存，尽管其性能小于HBR方法。在本文中，我们提出了一种基于自适应坐标的回归（ACR）损失，以提高CBR的面部比对的准确性。受主动形状模型（ASM）的启发，我们生成平滑面对象，与地面真相标志点相比，一组面部地标点具有较小的变化。然后，我们引入了一种方法，以通过比较地面真相标记点的分布和相应的平滑面对象来估计网络预测每个地标点的难度水平。我们提出的ACR损失可以根据预测面部中每个地标点的困难水平来适应其曲率和损失的影响。因此，ACR损失指导网络朝着具有挑战性的点而不是更容易的点，这提高了面部对齐任务的准确性。我们的广泛评估表明，拟议的ACR损失在预测各种面部图像中的面部标志点方面的能力。
Although deep neural networks have achieved reasonable accuracy in solving face alignment, it is still a challenging task, specifically when we deal with facial images, under occlusion, or extreme head poses. Heatmap-based Regression (HBR) and Coordinate-based Regression (CBR) are among the two mainly used methods for face alignment. CBR methods require less computer memory, though their performance is less than HBR methods. In this paper, we propose an Adaptive Coordinate-based Regression (ACR) loss to improve the accuracy of CBR for face alignment. Inspired by the Active Shape Model (ASM), we generate Smooth-Face objects, a set of facial landmark points with less variations compared to the ground truth landmark points. We then introduce a method to estimate the level of difficulty in predicting each landmark point for the network by comparing the distribution of the ground truth landmark points and the corresponding Smooth-Face objects. Our proposed ACR Loss can adaptively modify its curvature and the influence of the loss based on the difficulty level of predicting each landmark point in a face. Accordingly, the ACR Loss guides the network toward challenging points than easier points, which improves the accuracy of the face alignment task. Our extensive evaluation shows the capabilities of the proposed ACR Loss in predicting facial landmark points in various facial images.
