Visualizing and Quantifying Discriminative Features for Face Recognition
Gregory D. Castanon, Jeffrey Byrne · 2018
Deep convolutional networks have generated significant performance improvements in the domain of face recognition. However, these improvements do not provide insight into which facial features lead to classification decisions. In this paper, we explore the problem of visualizing discriminative information in faces, to show which properties of images and subjects influence classification. We compare six different techniques for computing a network saliency map, which identifies influential local features in an image, using a metric called the "hiding game" to directly evaluate these techniques on classification performance. Results show that contrastive excitation backprop (cEBP) [26] best localizes features that lead to face identification. However, these maps are nearly identical across subjects, which can result in an unstable network saliency map. We introduce a robust improvement called truncated cEBP and demonstrate the capability to predict the performance of a given map. Our evaluation provides the first application of network saliency to face recognition, and we provide a robust new tool for face recognition analysts to explore which facial regions lead to changes in match scores.