MFNet‐LE: Multilevel fusion network with Laplacian embedding for face presentation attacks detection

Abstract Face detection is playing a pivotal role for crowd counting and abnormal events detection. However, it is vulnerable to face presentation attacks by printed photos, videos, and 3D masks of real human faces. Although numerous detection techniques based on deep learning have been employed to...

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Autores principales:	Sijie Niu, Xiaofeng Qu, Junting Chen, Xizhan Gao, Tingwei Wang, Jiwen Dong
Formato:	article
Lenguaje:	EN
Publicado:	Wiley 2021
Materias:	Photography TR1-1050 Computer software QA76.75-76.765
Acceso en línea:	https://doaj.org/article/a0bac69a734d4a05bca0c7a3e2934aef
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spelling	oai:doaj.org-article:a0bac69a734d4a05bca0c7a3e2934aef2021-11-29T03:38:16ZMFNet‐LE: Multilevel fusion network with Laplacian embedding for face presentation attacks detection1751-96671751-965910.1049/ipr2.12308https://doaj.org/article/a0bac69a734d4a05bca0c7a3e2934aef2021-12-01T00:00:00Zhttps://doi.org/10.1049/ipr2.12308https://doaj.org/toc/1751-9659https://doaj.org/toc/1751-9667Abstract Face detection is playing a pivotal role for crowd counting and abnormal events detection. However, it is vulnerable to face presentation attacks by printed photos, videos, and 3D masks of real human faces. Although numerous detection techniques based on deep learning have been employed to address the problem of face presentation attacks, there are still several weaknesses in these approaches, such as high algorithm complexity and a lack of detection ability. To overcome these weaknesses, a method based on a multilevel fusion network with Laplacian embedding (MFNet‐LE) for the detection of face presentation attacks is proposed. First, a shallow network that contains just three layers was developed, which makes the model faster. Then, an optimised multilevel fusion strategy was developed to combine the input with the output of all previous layers to improve the detection ability of the method. Finally, a Laplacian embedding algorithm is introduced to maintain the inter‐class discrimination and penalise the intra‐class distance. Under the joint supervision of Laplacian loss and softmax loss, the proposed approach can obtain more discriminative features, which enhance the accuracy of attack detection. Experiments were conducted with three public databases for face presentation attacks: CASIA FASD, Idiap Replay Attack database and MSU USSA. The results demonstrate that the MFNet‐LE model can outperform the state‐of‐the‐art methods.Sijie NiuXiaofeng QuJunting ChenXizhan GaoTingwei WangJiwen DongWileyarticlePhotographyTR1-1050Computer softwareQA76.75-76.765ENIET Image Processing, Vol 15, Iss 14, Pp 3608-3622 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Photography TR1-1050 Computer software QA76.75-76.765
spellingShingle	Photography TR1-1050 Computer software QA76.75-76.765 Sijie Niu Xiaofeng Qu Junting Chen Xizhan Gao Tingwei Wang Jiwen Dong MFNet‐LE: Multilevel fusion network with Laplacian embedding for face presentation attacks detection
description	Abstract Face detection is playing a pivotal role for crowd counting and abnormal events detection. However, it is vulnerable to face presentation attacks by printed photos, videos, and 3D masks of real human faces. Although numerous detection techniques based on deep learning have been employed to address the problem of face presentation attacks, there are still several weaknesses in these approaches, such as high algorithm complexity and a lack of detection ability. To overcome these weaknesses, a method based on a multilevel fusion network with Laplacian embedding (MFNet‐LE) for the detection of face presentation attacks is proposed. First, a shallow network that contains just three layers was developed, which makes the model faster. Then, an optimised multilevel fusion strategy was developed to combine the input with the output of all previous layers to improve the detection ability of the method. Finally, a Laplacian embedding algorithm is introduced to maintain the inter‐class discrimination and penalise the intra‐class distance. Under the joint supervision of Laplacian loss and softmax loss, the proposed approach can obtain more discriminative features, which enhance the accuracy of attack detection. Experiments were conducted with three public databases for face presentation attacks: CASIA FASD, Idiap Replay Attack database and MSU USSA. The results demonstrate that the MFNet‐LE model can outperform the state‐of‐the‐art methods.
format	article
author	Sijie Niu Xiaofeng Qu Junting Chen Xizhan Gao Tingwei Wang Jiwen Dong
author_facet	Sijie Niu Xiaofeng Qu Junting Chen Xizhan Gao Tingwei Wang Jiwen Dong
author_sort	Sijie Niu
title	MFNet‐LE: Multilevel fusion network with Laplacian embedding for face presentation attacks detection
title_short	MFNet‐LE: Multilevel fusion network with Laplacian embedding for face presentation attacks detection
title_full	MFNet‐LE: Multilevel fusion network with Laplacian embedding for face presentation attacks detection
title_fullStr	MFNet‐LE: Multilevel fusion network with Laplacian embedding for face presentation attacks detection
title_full_unstemmed	MFNet‐LE: Multilevel fusion network with Laplacian embedding for face presentation attacks detection
title_sort	mfnet‐le: multilevel fusion network with laplacian embedding for face presentation attacks detection
publisher	Wiley
publishDate	2021
url	https://doaj.org/article/a0bac69a734d4a05bca0c7a3e2934aef
work_keys_str_mv	AT sijieniu mfnetlemultilevelfusionnetworkwithlaplacianembeddingforfacepresentationattacksdetection AT xiaofengqu mfnetlemultilevelfusionnetworkwithlaplacianembeddingforfacepresentationattacksdetection AT juntingchen mfnetlemultilevelfusionnetworkwithlaplacianembeddingforfacepresentationattacksdetection AT xizhangao mfnetlemultilevelfusionnetworkwithlaplacianembeddingforfacepresentationattacksdetection AT tingweiwang mfnetlemultilevelfusionnetworkwithlaplacianembeddingforfacepresentationattacksdetection AT jiwendong mfnetlemultilevelfusionnetworkwithlaplacianembeddingforfacepresentationattacksdetection
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MFNet‐LE: Multilevel fusion network with Laplacian embedding for face presentation attacks detection

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