Jin, BinYildirim, GökhanLau, CherylShaji, AppuOrtiz Segovia, MariaSüsstrunk, Sabine2015-03-032015-03-032015-03-03201510.1117/12.2076936https://infoscience.epfl.ch/handle/20.500.14299/111828WOS:000354081600027In 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.Face importanceface sizenumber of faces2D Gaussiansalient-object detectiontext::conference output::conference proceedings::conference paper