1 research outputs found
Salient Facial Features from Humans and Deep Neural Networks
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.Comment: 10 pages, 6 figure