Multimodality for Reliable Single Image Based Face Morphing Attack Detection
Face morphing attacks have demonstrated a high vulnerability on human observers and commercial off-the-shelf Face Recognition Systems (FRS), especially in the border control scenario. Therefore, detecting face morphing attacks is paramount to achieving a reliable and secure border control operation....
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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IEEE
2022-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9851419/ |
| _version_ | 1811310453616279552 |
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| author | Ramachandra Raghavendra Guoqiang Li |
| author_facet | Ramachandra Raghavendra Guoqiang Li |
| author_sort | Ramachandra Raghavendra |
| collection | DOAJ |
| description | Face morphing attacks have demonstrated a high vulnerability on human observers and commercial off-the-shelf Face Recognition Systems (FRS), especially in the border control scenario. Therefore, detecting face morphing attacks is paramount to achieving a reliable and secure border control operation. This work presents a novel framework for the Single image-based Morphing Attack Detection (S-MAD) based on the multimodal regions such as eyes, nose, and mouth. Each of these regions is processed using colour scale-space representation on which two different types of features are extracted using Binarised Statistical Image Features (BSIF) and Local Binary Features (LBP) techniques. These features are then fed to the classifiers such as Probabilistic Collaborative Representation Classifier (P-CRC) and Spectral Regression Kernel Discriminant Analysis (SRKDA). Their decisions are combined at score level to make the final decision. Extensive experiments are carried out on three different face morphing datasets to benchmark the performance of the proposed method with the existing methods. Further, the proposed method is benchmarked on the Bologna Online Evaluation Platform (BOEP). Obtained results demonstrate the improved performance of the proposed method over existing state-of-the-art methods. |
| first_indexed | 2024-04-13T09:58:55Z |
| format | Article |
| id | doaj.art-d09b9741b4b14252a023eff619c19a91 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-04-13T09:58:55Z |
| publishDate | 2022-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-d09b9741b4b14252a023eff619c19a912022-12-22T02:51:17ZengIEEEIEEE Access2169-35362022-01-0110824188243310.1109/ACCESS.2022.31967739851419Multimodality for Reliable Single Image Based Face Morphing Attack DetectionRamachandra Raghavendra0https://orcid.org/0000-0003-0484-3956Guoqiang Li1Institution of Information Security and Communication Technology (IIK), Norwegian University of Science and Technology (NTNU), Gjøvik, NorwayMOBAI AS, Gjøvik, NorwayFace morphing attacks have demonstrated a high vulnerability on human observers and commercial off-the-shelf Face Recognition Systems (FRS), especially in the border control scenario. Therefore, detecting face morphing attacks is paramount to achieving a reliable and secure border control operation. This work presents a novel framework for the Single image-based Morphing Attack Detection (S-MAD) based on the multimodal regions such as eyes, nose, and mouth. Each of these regions is processed using colour scale-space representation on which two different types of features are extracted using Binarised Statistical Image Features (BSIF) and Local Binary Features (LBP) techniques. These features are then fed to the classifiers such as Probabilistic Collaborative Representation Classifier (P-CRC) and Spectral Regression Kernel Discriminant Analysis (SRKDA). Their decisions are combined at score level to make the final decision. Extensive experiments are carried out on three different face morphing datasets to benchmark the performance of the proposed method with the existing methods. Further, the proposed method is benchmarked on the Bologna Online Evaluation Platform (BOEP). Obtained results demonstrate the improved performance of the proposed method over existing state-of-the-art methods.https://ieeexplore.ieee.org/document/9851419/Biometricsattacksface biometricsmorphing attacksmultimodal modality |
| spellingShingle | Ramachandra Raghavendra Guoqiang Li Multimodality for Reliable Single Image Based Face Morphing Attack Detection IEEE Access Biometrics attacks face biometrics morphing attacks multimodal modality |
| title | Multimodality for Reliable Single Image Based Face Morphing Attack Detection |
| title_full | Multimodality for Reliable Single Image Based Face Morphing Attack Detection |
| title_fullStr | Multimodality for Reliable Single Image Based Face Morphing Attack Detection |
| title_full_unstemmed | Multimodality for Reliable Single Image Based Face Morphing Attack Detection |
| title_short | Multimodality for Reliable Single Image Based Face Morphing Attack Detection |
| title_sort | multimodality for reliable single image based face morphing attack detection |
| topic | Biometrics attacks face biometrics morphing attacks multimodal modality |
| url | https://ieeexplore.ieee.org/document/9851419/ |
| work_keys_str_mv | AT ramachandraraghavendra multimodalityforreliablesingleimagebasedfacemorphingattackdetection AT guoqiangli multimodalityforreliablesingleimagebasedfacemorphingattackdetection |