Crowdsourcing is a popular tool for conducting subjective evaluations in uncontrolled environments and at low cost. In this paper, a crowdsourcing study is conducted to investigate the impact of High Dynamic Range (HDR) imaging on subjective face recognition accuracy. For that purpose, a dataset of HDR images of people depicted in high-contrast lighting conditions was created and their faces were manually cropped to construct a probe set of faces. Crowdsourcing-based face recognition was conducted for five differently tone-mapped versions of HDR faces and were compared to face recognition in a typical Low Dynamic Range alternative. A similar experiment was also conducted using three automatic face recognition algorithms. The comparative analysis results of face recognition by human subjects through crowdsourcing and machine vision face recognition show that HDR imaging affects the recognition results of human and computer vision approaches differently.