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.
翻译:尽管深心神经网络在解决脸部对齐方面已经取得了合理的准确性,但它仍然是一项艰巨的任务,特别是当我们处理面部图像时,在封闭状态下,或者在极端头部姿势下,这仍然是一项具有挑战性的任务。基于热映射的回归(HBR)和基于协调的回归(CBR)是主要用于面部对齐的两种方法之一。CBR方法需要较少计算机记忆,尽管其性能低于HBR方法。在本文件中,我们提议采用适应式坐标回归(ACR)损失,以提高 CBR对脸部对齐的准确性。在主动形状模型(ASM)的启发下,我们产生平滑脸部物体,这是一组面部位标志点,与地面真相里程碑点相比变化较少。然后我们采用一种方法,通过比较地面真相里程碑点的分布和相应的平滑面物体,来估计网络每个里程碑点的预测难度。我们提议的ACRL损失可以适应性调整其曲度和损失影响,根据对面每个标志点的难度水平进行预测。因此,ACRLL损失将指导网络对面的准确度进行广泛的预测。