We present a component-based framework for face detection and identification. The face detection and identification modules
share the same hierarchical architecture. They both consist of two layers of classifiers, a layer with a set of component
classifiers and a layer with a single combination classifier. The component classifiers independently detect/identify facial
parts in the image. Their outputs are passed the combination classifier which performs the final detection/identification
of the face.
We describe an algorithm which automatically learns two separate sets of facial components for the detection and identification
tasks. In experiments we compare the detection and identification systems to standard global approaches. The experimental
results clearly show that our component-based approach is superior to global approaches.
Keywords face detection - face identification - face recognition - object detection - object recognition - support vector - machines - components - fragments - parts - hierarchical classification