*''A robust classifier combined with an auto-associative network for completing partly occluded images'' [#p840059d]

**Takashi TAKAHASHI and Takio KURITA [#od088bd4]

Neural Networks, vol.18, pp.958--966, 2005

**Abstract [#z866df50]

This paper describes an approach for constructing a classifier which
is unaffected by occlusions in images.  We propose a method for
integrating an auto-associative network into a simple classifier.  As
the auto-associative network can recall the original image from a
partly occluded input image, we can employ it to detect occluded
regions and complete the input image by replacing those regions with
recalled pixels.  By iterating this reconstruction process, the
integrated network is able to classify target objects with occlusions
robustly.  To confirm the effectiveness of this method, we performed
experiments involving face image classification.  It is shown that the
classification performance is not decreased even if about 30% of
the face image is occluded.

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