Component-based face detection and verification

For face detection, while pose and illumination significantly change the global facial appearance, components of a face are less affected by these changes. Component detectors can accurately locate facial components, and component-based approaches can be used to check whether the geometric locations of the components comply with a face. This paper proposes a face detection and verification method using component-based online learning, which is based on using unsupervised clustering to find a set of templates specific to faces consisting of face component and their relations. The main difference from previously reported component-based face detection methods is the use of online learning, which is ideal for highly repetitive tasks. This results in faster and more accurate face detection, because system performance improves with continued use. Further, uncertainty is added by calculating the standard deviation of face components and their relations. A component-based method with uncertainty provides flexibility to allow variability to describe an object in appearance and geometry.