Robust visual recognition of high-level form in human observers

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Visual recognition is hard. Objects are seen in a variety of conditions differing in illumination, viewing distance, and orientation, which introduce drastic changes to their image. In addition, observer factorsfrom distortions introduced by the eye and the brain to varying visual experience over the observer's lifespaninfluence visual recognition. Despite significant advances, a comprehensive understanding of the impact of these factors in computations underlying visual recognition remains elusive. Our recent work showed that the visual system takes advantage of sustained regularities, e.g., those in size or expression, in the visual input to facilitate recognition of faces. In a naturalistic observation study, we acquired footage of daily visual experiences via eyewear-embedded cameras. Analysis of this footage revealed that most views of faces are from social interaction distances, i.e., visually large. Indeed, we found that face sizes most common in social interactions are associated with better recognition. Our work on regularities in size and blur, also uncovered evidence consistent with the overarching hypothesis of this proposal that the visual system adopts strategies to optimize recognition performance based on the most frequent types of visual input in the environment. Long-term objectives of my research are: (1) Describe various statistical regularities in human observers' visual input and their impact on recognition, including physical regularities, such as those that arise from the geometry of optics, as well as those that are consequences of the ways in which observers interact with their environment, (2) Uncover strategies of the visual system that utilize these regularities to facilitate recognition, and (3) Develop novel applications, such as computer programs, tailored to the human visual system to enhance recognition in low visibility conditions, e.g., of grainy images. My short-term objectives focus on specific statistical regularities that arise from (1) viewing distance to faces (e.g., visual size and blur), (2) ethnicities of faces most prevalent in the social environment, and (3) overall face exposure duration, in pursuit of the long-term objectives above. In the next 5-year cycle, we will continue to rely on our expertise in methodologies such as behavioural testing (e.g., visual psychophysics), computational modelling (e.g., ideal observers), and naturalistic observation of visual image statistics, (e.g., based on footage acquired through eyewear-embedded cameras). Highly qualified personnel at all levels will gain scientific and technical skills that will serve them in future careers in academia, industry and medicine. This work will advance our understanding of the fundamental principles underlying visual recognition in the brain and inform the development of tools that improve recognition of high-level form in a variety of settings from computer applications to visual aids in low-visibility conditions.