Learning Gaussian mixture model for saliency detection on face images

Abstract

The previous work has demonstrated that integrating top-down features in bottom-up saliency methods can improve the saliency prediction accuracy. Therefore, for face images, this paper proposes a saliency detection method based on Gaussian mixture model (GMM), which learns the distribution of saliency over face regions as the top-down feature. Specifically, we verify that fixations tend to cluster around facial features, when viewing images with large faces. Thus, the GMM is learnt from fixations of eye tracking data, for establishing the distribution of saliency in faces. Then, in our method, the top-down feature upon the the learnt GMM is combined with the conventional bottom-up features (i.e., color, intensity, and orientation), for saliency detection. Finally, experimental results validate that our method is capable of improving the accuracy of saliency prediction for face images.