Modeling the Contribution of Central Versus Peripheral Vision in Scene, Object, and Face Recognition
This work addresses a fundamental question in visual perception for cognitive science and computer vision, but it is incremental as it replicates and extends prior behavioral findings with computational modeling.
The study modeled the relative importance of central versus peripheral vision for recognizing scenes, objects, and faces using deep convolutional neural networks, finding that central vision is most important for faces, then objects, and least for scenes, with peripheral vision showing the opposite order.
It is commonly believed that the central visual field is important for recognizing objects and faces, and the peripheral region is useful for scene recognition. However, the relative importance of central versus peripheral information for object, scene, and face recognition is unclear. In a behavioral study, Larson and Loschky (2009) investigated this question by measuring the scene recognition accuracy as a function of visual angle, and demonstrated that peripheral vision was indeed more useful in recognizing scenes than central vision. In this work, we modeled and replicated the result of Larson and Loschky (2009), using deep convolutional neural networks. Having fit the data for scenes, we used the model to predict future data for large-scale scene recognition as well as for objects and faces. Our results suggest that the relative order of importance of using central visual field information is face recognition>object recognition>scene recognition, and vice-versa for peripheral information.