Face-aware Saliency Estimation Model for 360° Images

In this paper, a saliency estimation technique for omni-directional images is presented. Traditional approaches for estimating 360° image saliency rely on the exploitation of low and high-level image features, along with auxiliary data, such as head movement or eye-gazes. However, the image content plays an important role in saliency estimation. Based on this evidence, in the proposed method low-level features are combined with the detection of human faces. In this way it is possible to refine the saliency estimation based on the low-level features by assigning a larger weight to the regions containing faces. Experimental results on 360° image dataset show the effectiveness of the proposed approach.