QualFace: Adapting Deep Learning Face Recognition for ID and Travel Doc with Quality Assessment

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.

• Date: 01/08/2021
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• Featured In: BIOSIG 2021
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• Publication Type: Conference Papers
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• Author(s): João Tremoço, Iurii Medvedev, Nuno Gonçalves
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Towards understanding the character of quality sampling in deep learning face recognition

Face recognition has become one of the most important modalities of biometrics in recent years. It widely utilises deep learning computer vision tools and adopts large collections of unconstrained face images of celebrities for training. Such choice of the data is related to its public availability when existing document compliant face image collections are hardly accessible due to security and privacy issues. Such inconsistency between the training data and deploy scenario may lead to a leak in performance in biometric systems, which are developed speci cally for dealing with ID document compliant images. To mitigate this problem, we propose to regularise the training of the deep face recognition network with a speci c sample mining strategy, which penalises the samples by their estimated quality. In addition to several considered quality metrics in recent work, we also expand our deep learning strategy to other sophisticated quality estimation methods and perform experiments to better understand the nature of quality sampling. Namely, we seek for the penalising manner (sampling character) that better satis es the purpose of adapting deep learning face recognition for images of ID and travel documents. Extensive experiments demonstrate the ef ciency of the approach for ID document compliant face images.

• Date: 05/08/2022
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• Featured In: IETBiometrics
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• Publication Type: Journal Articles
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• Author(s): Iurii Medvedev, João Tremoço, Beatriz Mano, Luís Espírito-Santo and Nuno Gonçalves
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• DOI: 10.1049/bme2.12095
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