Abstract Visual attention is a fifield with a considerable history, with eye movement control and prediction forming an important subfifield. Fixation modeling in the past decades has been largely dominated computationally by a number of highly inflfluential bottom-up saliency models, such as the Itti-Koch-Niebur model. The accuracy of such models has dramatically increased recently due to deep learning. However, on static images the emphasis of these models has largely been based on non-ordered prediction of fifixations through a saliency map. Very few implemented models can generate temporally ordered human-like sequences of saccades beyond an initial fifixation point. Towards addressing these shortcomings we present STAR-FC, a novel multi-saccade generator based on the integration of central high-level and object-based saliency and peripheral lowerlevel feature-based saliency. We have evaluated our model using the CAT2000 database, successfully predicting human patterns of fifixation with equivalent accuracy and quality compared to what can be achieved by using one human sequence to predict another