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In view of the need to fuse the local information and the global information in an adaptive manner for reliable recognition, in this paper we investigate whether such external factors can be explicitly estimated and be used to boost the verification performance during fusion of the holistic and part-based methods.
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A feature injection layer is introduced to boost verification accuracy, showing the ability of the DCNN to learn a similarity metric leveraging external features.
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Both methods can represent the face as a set of feature vectors, which can be dealt with SVM or a boosting verification algorithm in this work.
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