2019 27th European Signal Processing Conference (EUSIPCO) 2019
DOI: 10.23919/eusipco.2019.8902533
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Dempster-Shafer Theory for Fusing Face Morphing Detectors

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Cited by 13 publications
(13 citation statements)
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“…Full Name: [92]. Following the initial work, several approaches were proposed, as indicated in [30], [71], [106], [107] have also been widely explored in the reported works. The use of micro-texture-based methods has shown reasonable performance on both digital and print-scan types of S-MAD.…”
Section: Passport Application Formmentioning
confidence: 99%
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“…Full Name: [92]. Following the initial work, several approaches were proposed, as indicated in [30], [71], [106], [107] have also been widely explored in the reported works. The use of micro-texture-based methods has shown reasonable performance on both digital and print-scan types of S-MAD.…”
Section: Passport Application Formmentioning
confidence: 99%
“…The first work in this direction was based on using pre-trained networks such as AlexNet and VGG18, in which the features are fused and classified to detect a morphing attack [93]. Following this, several deep CNN pre-trained networks, such as AlexNet, VGG19, VGG-Face16, GoogleNet, ResNet18, ResNet150, ResNet50, VGG-Face2 and OpenFace [52], [43], [122], [112], [107], [108], [113], [71], [112], have been explored. Although deep CNNs have shown better performance than hand-crafted texture descriptor-based MAD methods on both digital and print-scan data, the generalisation capability of these approaches is limited across different print and scan datasets [110].…”
Section: Passport Application Formmentioning
confidence: 99%
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“…In this work, we focus on single image based morphing attack detection (S-MAD) as it perfectly suits our dataset. MAD has been widely addressed in the literature by developing the techniques based on both deep learning [57], [58], [59] [60] [61] and non-deep learning [62] [19] [63] [64] approaches. Readers can refer to [65] for an exclusive survey on face MAD.…”
Section: Morphing Attack Detection Potentialmentioning
confidence: 99%
“…Such learned features are very powerful, but it is difficult to analyze what they describe or represent. Other blind detection methods are based on compositions of different detectors and fuse their predictions to obtain a more robust and accurate face morphing attack detector [28,29].…”
Section: Related Workmentioning
confidence: 99%