An eXplainable Artificial Intelligence Method for Deep Learning-Based Face Detection in Paintings

Abstract

Recently, despite the impressive success of deep learning, eXplainable Artificial Intelligence (XAI) is becoming increasingly important research area for ensuring transparency and trust in deep models, especially in the field of artwork analysis. In this paper, we conduct an analysis of major research contribution milestones in perturbation-based XAI methods and propose a novel iterative method based guided perturbations to explain face detection in Tenebrism painting images. Our method is independent of the model's architecture, outperforms the state-of-the-art method and requires very little computational resources (no need for GPUs). Quantitative and qualitative evaluation shows effectiveness of the proposed method.