One of the major tasks in human-computer interface applications, such as face recognition and video-telephony, is the exact localization of a face in an image.
In this chapter, I use the Neural Abstraction Pyramid architecture to solve this problem, even in presence of complex backgrounds, difficult lighting, and noise. The network is trained using a database of gray-scale still images to reproduce manually determined eye coordinates. It is able to generate reliable and accurate eye coordinates for unknown images by iteratively refining an initial solution.
The performance of the proposed approach is evaluated against a large test set. It is also shown that a moving face can be tracked. The fast network update allows for real-time operation.
KeywordsFace Detection Face Model Active Shape Model Face Localization Forward Projection
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