Morphing attacks have posed a severe threat to Face Recognition System (FRS). Despite the number of advancements reported in recent works, we note serious open issues such as independent benchmarking, generalizability challenges and considerations to age, gender, ethnicity that are inadequately addressed. Morphing Attack Detection (MAD) algorithms often are prone to generalization challenges as they are database dependent. The existing databases, mostly of semi-public nature, lack in diversity in terms of ethnicity, various morphing process and post-processing pipelines. Further, they do not reflect a realistic operational scenario for Automated Border Control (ABC) and do not provide a basis to test MAD on unseen data, in order to benchmark the robustness of algorithms. In this work, we present a new sequestered dataset for facilitating the advancements of MAD where the algorithms can be tested on unseen data in an effort to better generalize. The newly constructed dataset consists of facial images from 150 subjects from various ethnicities, age-groups and both genders. In order to challenge the existing MAD algorithms, the morphed images are with careful subject pre-selection created from the contributing images, and further post-processed to remove morphing artifacts. The images are also printed and scanned to remove all digital cues and to simulate a realistic challenge for MAD algorithms. Further, we present a new online evaluation platform to test algorithms on sequestered data. With the platform we can benchmark the morph detection performance and study the generalization ability. This work also presents a detailed analysis on various subsets of sequestered data and outlines open challenges for future directions in MAD research.

ID cards are uniquely linked to one individual via a printed or electronically provided facial image. Even though the face is treated as universal and distinctive characteristic, twins can weaken this distinctiveness because of their biological similarity. Also, humans might falsely recognise an unknown person as a friend - colloquially named a Dop-pelgänger. Recently it was demonstrated that this biological effect of similar data subjects can be purposefully established between two individuals in order to improve the vulnerability of the so-called morphing attack. This image manipulation technique creates a melted facial image which is similar to two or more data subjects. If embedded into an ID card, the manipulated reference image can be used by all participating individuals and thus the concept of a unique link is broken. This work elaborates the rather neglected part of selecting morph pairs based on a similarity score instead of a simple random assignment. It discusses the applicability of different possible algorithms. The finally developed approach considers complex real-world constraints while being executable in a reasonable amount of time and producing acceptable large morph sets. It is shown that this algorithm greatly increases the vulnerability of automated face recognition systems. Surprisingly, it also proves that an effective pre-selection of pairs questions the need of in-depth optimized morphing algorithms.

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.

The vulnerability of face recognition systems to attacks based on morphed biometric samples has been established in the recent past. Such attacks pose a severe security threat to a biometric recognition system in particular within the widely deployed border control applications. However, so far a reliable detection of morphed images has remained an unsolved research challenge. In this work, automated morph detection algorithms based on general purpose pattern recognition algorithms are benchmarked for two scenarios relevant in the context of fraud detection for electronic travel documents, i.e. single image (no-reference) and image pair (differential) morph detection. In the latter scenario a trusted live capture from an authentication attempt serves as additional source of information and, hence, the difference between features obtained from this face image and a potential morph can be estimated. A dataset of 2,206 ICAO compliant bona fide face images of the FRGCv2 face database is used to automatically generate 4,808 morphs. It is shown that in a differential scenario morph detectors which utilize a score level-based fusion of detection scores obtained from a single image and differences between image pairs generally outperform no-reference morph detectors with regard to the employed algorithms and used parameters. On average a relative improvement of more than 25% in terms of detection equal error rate is achieved.