In this work we study the varying importance of faces in images. Face importance is found to be affected by the size and number of faces present. We collected a dataset of 152 face images with faces in different size and number of faces. We conducted a crowdsourcing experiment where we asked people to label the important regions of the images. Analyzing the results from the experiment, we propose a simple face-importance model, which is a 2D Gaussian function, to quantitatively represent the influence of the size and number of faces on the perceived importance of faces. The face-importance model is then tested for the application of salient-object detection. For this application, we create a new salient-objects dataset, consisting of both face images and non-face images, and also through crowdsourcing we collect the ground truth. We demonstrate that our face-importance model helps us to better locate the important, thus salient, objects in the images and outperforms state-of-the-art salient-object detection algorithms.