Learning to Predict Saliency on Face Images

This paper proposes a novel method, which learns to detect saliency of face images. To be more specific, we obtain a database of eye tracking over extensive face images, via conducting an eye tracking experiment. With analysis on eye tracking database, we verify that the fixations tend to cluster around facial features, when viewing images with large faces. For modeling attention on faces and facial features, the proposed method learns the Gaussian mixture model (GMM) distribution from the fixations of eye tracking data as the top-down features for saliency detection of face images. Then, in our method, the top-down features (i.e., face and facial features) upon the the learnt GMM are linearly combined with the conventional bottom-up features (i.e., color, intensity, and orientation), for saliency detection. In the linear combination, we argue that the weights corresponding to top-down feature channels depend on the face size in images, and the relationship between the weights and face size is thus investigated via learning from the training eye tracking data. Finally, experimental results show that our learning-based method is able to advance state-of-the-art saliency prediction for face images. The corresponding database and code are available online: www.ee.buaa.edu.cn/xumfiles/saliency_detection.html.