Gender is playing an important role in the 2016 U.S. presidential election, especially with Hillary Clinton becoming the first female presidential nominee and Donald Trump being frequently accused of sexism. In this paper, we introduce computer vision to the study of gender politics and present an image-driven method that can measure the effects of gender in an accurate and timely manner. We first collect all the profile images of the candidates' Twitter followers. Then we train a convolutional neural network using images that contain gender labels. Lastly, we classify all the follower and unfollower images. Through two case studies, one on the `woman card' controversy and one on Sanders followers, we demonstrate how gender is informing the 2016 presidential election. Our framework of analysis can be readily generalized to other case studies and elections.