Explainable Artificial Intelligence (XAI) is an active research area to interpret a neural network's decision by ensuring transparency and trust in the task-specified learned models. In fact, despite the great success of deep learning networks in many fields, their adoption by practitioners presents some limits, one significant of them is the complex nature of these networks which prevents human comprehension of the decision-making process. This is especially the case in artworks analysis. To address this issue, we explore Detector Randomized Input Sampling for Explanation (DRISE), a visualization method for explainable artificial intelligence to comprehend and improve CNN-based face detector on Tenebrism painting images. The results obtained show local explanations for model's prediction and consequently offer insights into the model's decision-making. This paper will be of great help to researchers as a future support for explainability of object detection in other domain application.