Under natural viewing conditions, human observers shift their gaze to allocate processing resources to subsets of the visual input. Many computational models have aimed at predicting such voluntary attentional shifts. Although the importance of high level stimulus properties (higher order statistics, semantics) stands undisputed, most models are based on low-level features of the input alone. In this study we recorded eye-movements of human observers while they viewed photographs of natural scenes. About two thirds of the stimuli contained at least one person. We demonstrate that a combined model of face detection and low-level saliency clearly outperforms a low-level model in predicting locations humans fixate. This is reflected in our finding fact that observes, even when not instructed to look for anything particular, fixate on a face with a probability of over 80% within their first two fixations (500ms). Remarkably, the model's predictive performance in images that do not contain faces is not impaired by spurious face detector responses, which is suggestive of a bottom-up mechanism for face detection. In summary, we provide a novel computational approach which combines high level object knowledge (in our case: face locations) with low-level features to successfully predict the allocation of attentional resources.

A new bottom-up visual saliency model, Graph-Based Visual Saliency (GBVS), is proposed. It consists of two steps: rst forming activation maps on certain feature channels, and then normalizing them in a way which highlights conspicuity and admits combination with other maps. The model is simple, and biologically plausible insofar as it is naturally parallelized. This model powerfully predicts human xations on 749 variations of 108 natural images, achieving 98% of the ROC area of a human-based control, whereas the classical algorithms of Itti & Koch ([2], [3], [4]) achieve only 84%.

A new bottom-up visual saliency model, Graph-Based Visual Saliency (GBVS), is proposed. It consists of two steps: rst forming activation maps on certain feature channels, and then normalizing them in a way which highlights conspicuity and admits combination with other maps. The model is simple, and biologically plausible insofaras it is naturally parallelized. This model powerfully predicts human xations on 749 variations of 108 natural images, achieving 98% of the ROC area of a human-based control, whereas the classical algorithms of Itti & Koch ([2], [3], [4]) achieve only 84%.