High dynamic range (HDR) imaging has potential to facili- tate computer vision tasks such as image matching where lighting trans- formations hinder the matching performance. However, little has been done to quantify the gains with different possible HDR representations for vision algorithms like feature extraction. In this paper, we evaluate the performance of the full feature extraction pipeline, including detection and description, on ten different image representations: low dynamic range (LDR), seven different tone mapped (TM) HDR and two HDR imaging (linear and log encoded) representations. We measure the impact of using these different representations for feature matching using mean average precision (mAP) scores on four illumination change datasets. We perform feature extraction using four popular schemes in the literature: SIFT, SURF, BRISK, FREAK. With respect to previous studies, our observations confirm the advantages of HDR over conventional LDR imagery, and the fact that HDR linear values are not appropriate for vision tasks. However, HDR representations that work best for keypoint detection are not necessarily optimal when the full feature extraction is taken into account.