Landmark labeling of training images is essential for many learning tasks in computer vision, such as object detection, tracking, and alignment. Image labeling is typically conducted manually, which is both labor-intensive and error-prone. To improve this process, this paper proposes a new approach to estimate a set of landmarks for a large image ensemble with only a small number of manually labeled images from the ensemble. Our approach, named semi-supervised least-squares congealing, aims to minimize an objective function defined on both labeled and unlabeled images. A shape model is learnt on-line to constrain the landmark configuration. We also employ a partitioning strategy to allow coarse-to-fine landmark estimation. Extensive experiments on facial images show that our approach can reliably and accurately label landmarks for a large image ensemble starting from a small number of manually labeled images, under various challenging scenarios.
['Yan Tong', 'Xiaoming Liu', 'Frederick Wilson Wheeler', 'Peter Henry Tu']
Automatic facial landmark labeling with minimal supervision