Face Localization and Tracking in the Neural Abstraction Pyramid
- Author: Sven Behnke
- Neural Computing and Applications, 14(2), pp. 97-103, Springer,
July 2005.
(Published online Nov. 2004).
- Abstract:
One of the major tasks in some
human-computer interface applications, such as face recognition and
video telephony,
is to localize a human face in an image.
In this paper, we propose to use
hierarchical neural networks with local recurrent connectivity to solve
this
task not only in unambiguous situations, but also in the presence of
complex
backgrounds, difficult lighting, and noise.
The networks are trained using a
database of gray-scale still images and manually determined eye
coordinates. They are able to produce
reliable and
accurate eye coordinates for unknown images by iteratively refining
initial
solutions. Because the networks process
entire
images, there is no need for any time-consuming scanning across
positions and
scales.
Furthermore, the fast network updates
allow for real-time face tracking. In this case, the networks are
trained using
still images that move in random directions. The trained networks are
able to
accurately track the eye positions in test image sequences.
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