Multi-Domain Feature Alignment for Face Anti-Spoofing

Face anti-spoofing is critical for enhancing the robustness of face recognition systems against presentation attacks. Existing methods predominantly rely on binary classification tasks. Recently, methods based on domain generalization have yielded promising results. However, due to distribution disc...
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Gespeichert in:

Autor*in:	Shizhe Zhang [verfasserIn] Wenhui Nie [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2023

Schlagwörter:	multi-domain feature alignment domain generalization (MADG) face anti-spoofing feature alignment multiple source domain domain generalization transfer learning

Übergeordnetes Werk:	In: Sensors - MDPI AG, 2003, 23(2023), 8, p 4077
Übergeordnetes Werk:	volume:23 ; year:2023 ; number:8, p 4077

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3390/s23084077

Katalog-ID:	DOAJ089774000

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520			\|a Face anti-spoofing is critical for enhancing the robustness of face recognition systems against presentation attacks. Existing methods predominantly rely on binary classification tasks. Recently, methods based on domain generalization have yielded promising results. However, due to distribution discrepancies between various domains, the differences in the feature space related to the domain considerably hinder the generalization of features from unfamiliar domains. In this work, we propose a multi-domain feature alignment framework (MADG) that addresses poor generalization when multiple source domains are distributed in the scattered feature space. Specifically, an adversarial learning process is designed to narrow the differences between domains, achieving the effect of aligning the features of multiple sources, thus resulting in multi-domain alignment. Moreover, to further improve the effectiveness of our proposed framework, we incorporate multi-directional triplet loss to achieve a higher degree of separation in the feature space between fake and real faces. To evaluate the performance of our method, we conducted extensive experiments on several public datasets. The results demonstrate that our proposed approach outperforms current state-of-the-art methods, thereby validating its effectiveness in face anti-spoofing.
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spelling	10.3390/s23084077 doi (DE-627)DOAJ089774000 (DE-599)DOAJ807fa81894014124943e0c49b20f5c93 DE-627 ger DE-627 rakwb eng TP1-1185 Shizhe Zhang verfasserin aut Multi-Domain Feature Alignment for Face Anti-Spoofing 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Face anti-spoofing is critical for enhancing the robustness of face recognition systems against presentation attacks. Existing methods predominantly rely on binary classification tasks. Recently, methods based on domain generalization have yielded promising results. However, due to distribution discrepancies between various domains, the differences in the feature space related to the domain considerably hinder the generalization of features from unfamiliar domains. In this work, we propose a multi-domain feature alignment framework (MADG) that addresses poor generalization when multiple source domains are distributed in the scattered feature space. Specifically, an adversarial learning process is designed to narrow the differences between domains, achieving the effect of aligning the features of multiple sources, thus resulting in multi-domain alignment. Moreover, to further improve the effectiveness of our proposed framework, we incorporate multi-directional triplet loss to achieve a higher degree of separation in the feature space between fake and real faces. To evaluate the performance of our method, we conducted extensive experiments on several public datasets. The results demonstrate that our proposed approach outperforms current state-of-the-art methods, thereby validating its effectiveness in face anti-spoofing. multi-domain feature alignment domain generalization (MADG) face anti-spoofing feature alignment multiple source domain domain generalization transfer learning Chemical technology Wenhui Nie verfasserin aut In Sensors MDPI AG, 2003 23(2023), 8, p 4077 (DE-627)331640910 (DE-600)2052857-7 14248220 nnns volume:23 year:2023 number:8, p 4077 https://doi.org/10.3390/s23084077 kostenfrei https://doaj.org/article/807fa81894014124943e0c49b20f5c93 kostenfrei https://www.mdpi.com/1424-8220/23/8/4077 kostenfrei https://doaj.org/toc/1424-8220 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2111 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 23 2023 8, p 4077
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allfieldsSound	10.3390/s23084077 doi (DE-627)DOAJ089774000 (DE-599)DOAJ807fa81894014124943e0c49b20f5c93 DE-627 ger DE-627 rakwb eng TP1-1185 Shizhe Zhang verfasserin aut Multi-Domain Feature Alignment for Face Anti-Spoofing 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Face anti-spoofing is critical for enhancing the robustness of face recognition systems against presentation attacks. Existing methods predominantly rely on binary classification tasks. Recently, methods based on domain generalization have yielded promising results. However, due to distribution discrepancies between various domains, the differences in the feature space related to the domain considerably hinder the generalization of features from unfamiliar domains. In this work, we propose a multi-domain feature alignment framework (MADG) that addresses poor generalization when multiple source domains are distributed in the scattered feature space. Specifically, an adversarial learning process is designed to narrow the differences between domains, achieving the effect of aligning the features of multiple sources, thus resulting in multi-domain alignment. Moreover, to further improve the effectiveness of our proposed framework, we incorporate multi-directional triplet loss to achieve a higher degree of separation in the feature space between fake and real faces. To evaluate the performance of our method, we conducted extensive experiments on several public datasets. The results demonstrate that our proposed approach outperforms current state-of-the-art methods, thereby validating its effectiveness in face anti-spoofing. multi-domain feature alignment domain generalization (MADG) face anti-spoofing feature alignment multiple source domain domain generalization transfer learning Chemical technology Wenhui Nie verfasserin aut In Sensors MDPI AG, 2003 23(2023), 8, p 4077 (DE-627)331640910 (DE-600)2052857-7 14248220 nnns volume:23 year:2023 number:8, p 4077 https://doi.org/10.3390/s23084077 kostenfrei https://doaj.org/article/807fa81894014124943e0c49b20f5c93 kostenfrei https://www.mdpi.com/1424-8220/23/8/4077 kostenfrei https://doaj.org/toc/1424-8220 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2111 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 23 2023 8, p 4077
language	English
source	In Sensors 23(2023), 8, p 4077 volume:23 year:2023 number:8, p 4077
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topic_facet	multi-domain feature alignment domain generalization (MADG) face anti-spoofing feature alignment multiple source domain domain generalization transfer learning Chemical technology
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container_title	Sensors
authorswithroles_txt_mv	Shizhe Zhang @@aut@@ Wenhui Nie @@aut@@
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callnumber-first	T - Technology
author	Shizhe Zhang
spellingShingle	Shizhe Zhang misc TP1-1185 misc multi-domain feature alignment domain generalization (MADG) misc face anti-spoofing misc feature alignment misc multiple source domain misc domain generalization misc transfer learning misc Chemical technology Multi-Domain Feature Alignment for Face Anti-Spoofing
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topic_title	TP1-1185 Multi-Domain Feature Alignment for Face Anti-Spoofing multi-domain feature alignment domain generalization (MADG) face anti-spoofing feature alignment multiple source domain domain generalization transfer learning
topic	misc TP1-1185 misc multi-domain feature alignment domain generalization (MADG) misc face anti-spoofing misc feature alignment misc multiple source domain misc domain generalization misc transfer learning misc Chemical technology
topic_unstemmed	misc TP1-1185 misc multi-domain feature alignment domain generalization (MADG) misc face anti-spoofing misc feature alignment misc multiple source domain misc domain generalization misc transfer learning misc Chemical technology
topic_browse	misc TP1-1185 misc multi-domain feature alignment domain generalization (MADG) misc face anti-spoofing misc feature alignment misc multiple source domain misc domain generalization misc transfer learning misc Chemical technology
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title	Multi-Domain Feature Alignment for Face Anti-Spoofing
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title_full	Multi-Domain Feature Alignment for Face Anti-Spoofing
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title_sort	multi-domain feature alignment for face anti-spoofing
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title_auth	Multi-Domain Feature Alignment for Face Anti-Spoofing
abstract	Face anti-spoofing is critical for enhancing the robustness of face recognition systems against presentation attacks. Existing methods predominantly rely on binary classification tasks. Recently, methods based on domain generalization have yielded promising results. However, due to distribution discrepancies between various domains, the differences in the feature space related to the domain considerably hinder the generalization of features from unfamiliar domains. In this work, we propose a multi-domain feature alignment framework (MADG) that addresses poor generalization when multiple source domains are distributed in the scattered feature space. Specifically, an adversarial learning process is designed to narrow the differences between domains, achieving the effect of aligning the features of multiple sources, thus resulting in multi-domain alignment. Moreover, to further improve the effectiveness of our proposed framework, we incorporate multi-directional triplet loss to achieve a higher degree of separation in the feature space between fake and real faces. To evaluate the performance of our method, we conducted extensive experiments on several public datasets. The results demonstrate that our proposed approach outperforms current state-of-the-art methods, thereby validating its effectiveness in face anti-spoofing.
abstractGer	Face anti-spoofing is critical for enhancing the robustness of face recognition systems against presentation attacks. Existing methods predominantly rely on binary classification tasks. Recently, methods based on domain generalization have yielded promising results. However, due to distribution discrepancies between various domains, the differences in the feature space related to the domain considerably hinder the generalization of features from unfamiliar domains. In this work, we propose a multi-domain feature alignment framework (MADG) that addresses poor generalization when multiple source domains are distributed in the scattered feature space. Specifically, an adversarial learning process is designed to narrow the differences between domains, achieving the effect of aligning the features of multiple sources, thus resulting in multi-domain alignment. Moreover, to further improve the effectiveness of our proposed framework, we incorporate multi-directional triplet loss to achieve a higher degree of separation in the feature space between fake and real faces. To evaluate the performance of our method, we conducted extensive experiments on several public datasets. The results demonstrate that our proposed approach outperforms current state-of-the-art methods, thereby validating its effectiveness in face anti-spoofing.
abstract_unstemmed	Face anti-spoofing is critical for enhancing the robustness of face recognition systems against presentation attacks. Existing methods predominantly rely on binary classification tasks. Recently, methods based on domain generalization have yielded promising results. However, due to distribution discrepancies between various domains, the differences in the feature space related to the domain considerably hinder the generalization of features from unfamiliar domains. In this work, we propose a multi-domain feature alignment framework (MADG) that addresses poor generalization when multiple source domains are distributed in the scattered feature space. Specifically, an adversarial learning process is designed to narrow the differences between domains, achieving the effect of aligning the features of multiple sources, thus resulting in multi-domain alignment. Moreover, to further improve the effectiveness of our proposed framework, we incorporate multi-directional triplet loss to achieve a higher degree of separation in the feature space between fake and real faces. To evaluate the performance of our method, we conducted extensive experiments on several public datasets. The results demonstrate that our proposed approach outperforms current state-of-the-art methods, thereby validating its effectiveness in face anti-spoofing.
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title_short	Multi-Domain Feature Alignment for Face Anti-Spoofing
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