论文标题
验证基于深度学习的面部表达识别的决策
Verifying Deep Learning-based Decisions for Facial Expression Recognition
论文作者
论文摘要
具有高性能的神经网络仍然可能偏向非相关特征。但是，可靠性和鲁棒性对于诸如临床疼痛治疗之类的高风险领域尤为重要。因此，我们提出了一个验证管道，该管道包括三个步骤。首先，我们将面部表情分类为神经网络。接下来，我们将层面相关性传播应用于创建基于像素的解释。最后，我们基于面部区域的边界框方法量化了这些视觉解释。尽管我们的结果表明神经网络可实现最先进的结果，但视觉解释的评估表明，可能不考虑相关的面部区域。
Neural networks with high performance can still be biased towards non-relevant features. However, reliability and robustness is especially important for high-risk fields such as clinical pain treatment. We therefore propose a verification pipeline, which consists of three steps. First, we classify facial expressions with a neural network. Next, we apply layer-wise relevance propagation to create pixel-based explanations. Finally, we quantify these visual explanations based on a bounding-box method with respect to facial regions. Although our results show that the neural network achieves state-of-the-art results, the evaluation of the visual explanations reveals that relevant facial regions may not be considered.
