Martial Hebert

When we see other humans, we can quickly make judgements regarding many aspects, including their demographic description and identity if they are familiar to us. We can answer questions related to the activities of, emotional states of, and relationships between people in an image. We draw conclusions based not just on what we see, but also from a lifetime of experience of living and interacting with other people. In this dissertation, we propose contextual features and models for understanding images of people with the objective of providing computers with access to the same contextual information that humans use. We show through a series of visual experiments that humans can exploit contextual knowledge to understand images of people. Recognizing other people becomes easier when the full body is shown instead of just the face. Social context is exploited to assign faces to corresponding first names, and age and gender recognition is improved when subjects see a face from an image in context with the other faces from the image instead of only a single face. In this dissertation, we provide contextual features and probabilistic frameworks to allow the computer to interpret images of people with contextual information. We propose features re