A Component-based Framework for Face Detection and Identification

Authors:
Bernd Heisele;Thomas Serre;T. Poggio
Affiliations:
Honda Research Institute USA in Cambridge and the Center for Biological and Computational Learning at M.I.T. Cambridge, Cambridge, USA;the Department of Brain and Cognitive Sciences and the Computer Science and Artificial Intelligence Laboratory at M.I.T., McGovern Institute for Brain Research, the Center for Biological and Compu ...;the Department of Brain and Cognitive Sciences and the Computer Science and Artificial Intelligence Laboratory at M.I.T., McGovern Institute for Brain Research, the Center for Biological and Compu ...
Venue:
International Journal of Computer Vision
Year:
2007

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Abstract

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.