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Abstract:
A new learning model based on autoassociative neural networks
is developed and applied to face detection. To extend the detection
ability in orientation and to decrease the number of false alarms,
different combinations of networks are tested: ensemble,
conditional ensemble and conditional mixture of networks. The use
of a conditional mixture of networks allows to obtain state of the
art results on different benchmark face databases.
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