Auto Zoom Crop from Face Detection and Facial Features

The automatic recomposition of a digital photograph to a more pleasing composition or alternate aspect ratio is a very powerful concept. The human face is arguably one of the most frequently photographed and important subjects. Although evidence suggests only a minority of photos contain faces, the vast majority of images used in consumer photobooks contain faces. Face detection and facial understanding algorithms are becoming ubiquitous to the computational photography community and facial features have a dominating influence on both aesthetic and compositional properties of the displayed image. We introduce a fully automatic recomposition algorithm, capable of zooming in to a more pleasing composition, re-trimming to alternate aspect ratios, or a combination thereof. We use facial bounding boxes, input and output aspect ratios, along with derived composition rules to introduce a facecrop algorithm with superior performance to more complex saliency or region of interest detection algorithms. We further introduce sophisticated facial understanding rules to improve user satisfaction further. We demonstrate through psychophysical studies the improved subjective quality of our method compared to state-of-the-art techniques.