Extended excitation backprop with gradient weighting: A general visualization solution for understanding heterogeneous face recognition

Visualization methods have been used to reveal areas of images which influence the decision making of machine learning models, thereby helping to understand and diagnose the learned models and suggest ways to improve their performance. This concept is termed Explainable Artificial Intelligence. In this work, we focus on visualization methods for metric-learning based neural networks. We propose a gradient-weighted extended Excitation Back-Propagation (gweEBP) method that integrates the gradient information during its backpropagation for the accurate investigation of embedding networks. We perform an extensive evaluation of our gweEBP, and seven other visualization methods, on two neural networks, trained for heterogeneous face recognition. The evaluation is performed over two publicly available cross-modality datasets using two evaluation methods termed the “hiding game” and the “inpainting game”. Our experiments showed that the proposed method outperforms the competing methods in both games in most cases. Additionally, our comprehensive study also provides a benchmark for comparing visualization techniques, which may help other researchers develop new techniques and perform comparative studies on them.