Detecting Morphed Face Images Using Facial Landmarks

Scherhag, Ulrich; Budhrani, Dhanesh; Gomez‐Barrero, Marta; Busch, Christoph

doi:10.1007/978-3-319-94211-7_48

Cited by 45 publications

(39 citation statements)

References 10 publications

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“…The additional information of the trusted live capture might improve the performance and robustness of the detection algorithm. Further, algorithms explicitly utilizing this additional information have been introduced in [9], [11], where the distances between features of facial landmarks are estimated.…”

Section: B Differential Madmentioning

confidence: 99%

Deep Face Representations for Differential Morphing Attack Detection

Scherhag

Rathgeb

Merkle³

et al. 2020

IEEE Trans.Inform.Forensic Secur.

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133

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The vulnerability of facial recognition systems to face morphing attacks is well known. Many different approaches for morphing attack detection (MAD) have been proposed in the scientific literature. However, the MAD algorithms proposed so far have mostly been trained and tested on datasets whose distributions of image characteristics are either very limited (e.g., only created with a single morphing tool) or rather unrealistic (e.g., no print-scan transformation). As a consequence, these methods easily overfit on certain image types and the results presented cannot be expected to apply to real-world scenarios. For example, the results of the latest NIST FRVT MORPH show that the majority of submitted MAD algorithms lacks robustness and performance when considering unseen and challenging datasets. In this work, subsets of the FERET and FRGCv2 face databases are used to create a realistic database for training and testing of MAD algorithms, containing a large number of ICAO-compliant bona fide facial images, corresponding unconstrained probe images, and morphed images created with four different face morphing tools. Furthermore, multiple post-processings are applied on the reference images, e.g., print-scan and JPEG2000 compression. On this database, previously proposed differential morphing algorithms are evaluated and compared. In addition, the application of deep face representations for differential MAD algorithms is investigated. It is shown that algorithms based on deep face representations can achieve very high detection performance (less than 3% D-EER) and robustness with respect to various post-processings. Finally, the limitations of the developed methods are analyzed.

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Section: B Differential Madmentioning

confidence: 99%

Deep Face Representations for Differential Morphing Attack Detection

Scherhag

Rathgeb

Merkle³

et al. 2020

IEEE Trans.Inform.Forensic Secur.

Self Cite

133

View full text Add to dashboard Cite

show abstract

“…Differential MAD needs two images for morphing detection and often proposes solutions for similar ABC systems where two images of identities are available. For instance, Scherhag [58] seeks SIFT descriptors in the ID passport image and the in situ image. Once the descriptors of both images are detected, they are compared.…”

Section: Previous Workmentioning

confidence: 99%

“…This fake image is based on the surrogate image and the ABC person's face. The approach is similar to the previous research study in [58], but this time, the amount and position of the face landmark detected are compared [59].…”

Section: Previous Workmentioning

confidence: 99%

Border Control Morphing Attack Detection with a Convolutional Neural Network De-morphing Approach

et al. 2020

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Currently, the use of biometric identification, automated or semiautomated, is a reality. For this reason, the number of attacks has increased in such systems. One of the most common biometric attacks is the presentation attack (PA) because it is relatively easy to perform. Automated border control (ABC) is a clear target for phishers. Concerning biometric attacks, morphing is one of the most threatening attacks because authentication systems are usually unable to correctly detect them. In this attack, a fake face is generated with the morphing and blending of two different subjects (genuine and phisher), and the image result is stored in the passport. These attacks can generate risky situations in cases of border crossings where an ABC system should perform identification tasks. This research work proposes a de-morphing architecture that is founded on a convolutional neural network (CNN) architecture. This technique is based on the use of two images: the potentially morphed image stored in the passport, and the snapshot of the person located in the ABC system. The goal of the de-morphing process is to unravel the chip image. If the chip image is a morphed one, the revealing process between the in vivo image and the morphed chip image will return a different facial identity to the person located in the ABC system, and the impostor will be uncovered in situ. If the chip image is a non-morphing image, the resulting image will be similar to a genuine passenger. Therefore, the information obtained is considered at the border crossing. The equal error rate (EER) achieved is very low compared to the literature values published to date. The accomplished outcomes endorse a robust method that provides high accuracy rates without taking into account the quality of images used. This key point is crucial to plausible deployment plans in areas such as ABC. INDEX TERMS ABC, biometric systems, de-morphing, neural networks, MAD.

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“…In [9] and [10] the locations of facial landmarks in a suspected morph are compared with the landmark locations in an available reference image. The shift between the two sets of landmark locations tends to be smaller for a pair of images with the same identity than if one of the two images is a morph.…”

Section: Differential Morphing Attack Detectionmentioning

confidence: 99%

“…The shift between the two sets of landmark locations tends to be smaller for a pair of images with the same identity than if one of the two images is a morph. In [9] the euclidean distance and angle of the landmark shifts are used and a D-EER of 32.7% is recorded. In [10] the directed distances of the landmarks shifts are used and a spectacular D-EER of 0.00% is reported.…”

Section: Differential Morphing Attack Detectionmentioning

confidence: 99%

Computer Science & Information Technology (CS & IT)

2020

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Detecting Morphed Face Images Using Facial Landmarks

Cited by 45 publications

References 10 publications

Deep Face Representations for Differential Morphing Attack Detection

Deep Face Representations for Differential Morphing Attack Detection

Border Control Morphing Attack Detection with a Convolutional Neural Network De-morphing Approach

Computer Science & Information Technology (CS & IT)

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