Modeling the Importance of Faces in Natural Images

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.


Published in:
Human Vision and Electronic Imaging XX - Proceedings of SPIE
Presented at:
Human Vision and Electronic Imaging XX, IS&T/SPIE Electronic Imaging, San Francisco, USA, February 9 - 12, 2015
Year:
2015
Publisher:
Bellingham, Spie-Int Soc Optical Engineering
ISBN:
978-1-62841-484-4
Keywords:
Laboratories:




 Record created 2015-03-03, last modified 2018-03-17

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