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Abstract:
A novel learning approach for human face detection using a
network of linear units is presented. The SNoW learning
architecture is a sparse network of linear functions over a
pre-defined or incrementally learned feature space and is
specifically tailored for learning in the presence of a very large
number of features. A wide range of face images in different poses,
with different expressions and under different lighting conditions
are used as a training set to capture the variations of human
faces. Experimental results on commonly used benchmark data sets of
a wide range of face images show that the SNoW-based approach
outperforms methods that use neural networks, Bayesian methods,
support vector machines and others. Furthermore, learning and
evaluation using the SNoW-based method are significantly more
efficient than with other methods.
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