Using context to enhance the understanding of face images

Faces are special objects of interest. Developing automated systems for detecting and recognizing faces is useful in a variety of application domains including providing aid to visually-impaired people and managing large-scale collections of images. Humans have a remarkable ability to detect and identify faces in an image, but related automated systems perform poorly in real-world scenarios, particularly on faces that are difficult to detect and recognize. Why are humans so good? There is general agreement in the cognitive science community that the human brain uses the context of the scene shown in an image to solve the difficult cases of detection and recognition. This dissertation focuses on emulating this approach by using different kinds of contextual information for improving the performance of various approaches for face detection and face recognition. For the face detection problem, we describe an algorithm that employs the easy-to-detect faces in an image to find the difficult-to-detect faces in the same image. For the face recognition problem, we present a joint probabilistic model for image-caption pairs. This model solves the difficult cases of face recognition in an image by using the context generated from the caption associated with the same image. Finally, we present an effective solution for classifying the scene shown in an image, which provides useful context for both of the face detection and recognition problems