论文标题
特权归因限制了面部表达识别的深层网络
Privileged Attribution Constrained Deep Networks for Facial Expression Recognition
论文作者
论文摘要
面部表达识别（FER）在许多研究领域至关重要，因为它使机器能够更好地了解人类的行为。 FER方法面临着相对较小的数据集和嘈杂数据的问题，这些数据不允许经典网络良好地概括。为了减轻这些问题，我们指导该模型专注于眼睛，嘴巴或眉毛等特定面部区域，我们认为这是决定面部表情的决定性的。我们提出了特权归因损失（PAL），该方法通过鼓励其归因图与面部标志形成的热图相对应，从而将模型的注意力引向最显着的面部区域。此外，我们介绍了几种渠道策略，使该模型具有更高的自由度。所提出的方法独立于骨干架构，并且在测试时不需要其他语义信息。最后，实验结果表明，所提出的PAL方法的表现优于RAF-DB和Actionnet上的最新方法。
Facial Expression Recognition (FER) is crucial in many research domains because it enables machines to better understand human behaviours. FER methods face the problems of relatively small datasets and noisy data that don't allow classical networks to generalize well. To alleviate these issues, we guide the model to concentrate on specific facial areas like the eyes, the mouth or the eyebrows, which we argue are decisive to recognise facial expressions. We propose the Privileged Attribution Loss (PAL), a method that directs the attention of the model towards the most salient facial regions by encouraging its attribution maps to correspond to a heatmap formed by facial landmarks. Furthermore, we introduce several channel strategies that allow the model to have more degrees of freedom. The proposed method is independent of the backbone architecture and doesn't need additional semantic information at test time. Finally, experimental results show that the proposed PAL method outperforms current state-of-the-art methods on both RAF-DB and AffectNet.
