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QualFace: Adapting Deep Learning Face Recognition for ID and Travel Documents with Quality Assessment
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Datum
2021
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Gesellschaft für Informatik e.V.
Zusammenfassung
Modern face recognition biometrics widely rely on deep neural networks that are usually trained on
large collections of wild face images of celebrities. This choice of the data is related with its public
availability in a situation when existing ID document compliant face image datasets (usually stored
by national institutions) are hardly accessible due to continuously increasing privacy restrictions.
However this may lead to a leak in performance in systems developed specifically for ID document
compliant images. In this work we proposed a novel face recognition approach for mitigating that
problem. To adapt deep face recognition network for document security purposes, we propose to
regularise the training process with specific sample mining strategy which penalises the samples
by their estimated quality, where the quality metric is proposed by our work and is related to the
specific case of face images for ID documents. We perform extensive experiments and demonstrate
the efficiency of proposed approach for ID document compliant face images.