论文标题
人类和深度神经网络的显着面部特征
Salient Facial Features from Humans and Deep Neural Networks
论文作者
论文摘要
在这项工作中，我们探讨了人类和卷积神经网络（Convnets）使用的特征来对面部进行分类。我们使用引导的反向传播（GB）来可视化影响转弯输出最大的面部特征。我们探索如何最好地将GB用于此目的。我们使用人类的情报任务来找出人类认为对识别特定个人最重要的面部特征。我们探讨了从人类那里收集的显着信息和从Convnets收集的显着信息之间的差异。 人类在采用有关面部特征的可用信息以区分面部面孔的偏见。研究表明，这些偏见受神经学发展和每个人的社会经验的影响。近年来，计算机视觉社区在具有深度神经网络模型的许多面部处理任务中取得了人类水平的性能。由于建筑选择和培训数据分布，这些面部处理系统还受到系统的偏见。
In this work, we explore the features that are used by humans and by convolutional neural networks (ConvNets) to classify faces. We use Guided Backpropagation (GB) to visualize the facial features that influence the output of a ConvNet the most when identifying specific individuals; we explore how to best use GB for that purpose. We use a human intelligence task to find out which facial features humans find to be the most important for identifying specific individuals. We explore the differences between the saliency information gathered from humans and from ConvNets. Humans develop biases in employing available information on facial features to discriminate across faces. Studies show these biases are influenced both by neurological development and by each individual's social experience. In recent years the computer vision community has achieved human-level performance in many face processing tasks with deep neural network-based models. These face processing systems are also subject to systematic biases due to model architectural choices and training data distribution.
