Fingerprint morphing is the process of combining two or more distinct fingerprints to create a new, morphed fingerprint that includes identity-related characteristics of all constituent fingerprints. Previously, this was done by either applying a model-based minutiae-oriented approach or a data-driven approach based on a Generative Adversarial Network (GAN). The model-based approach provides the ability to manage the number of minutiae coming from the fingerprints, but the resulting fingerprint often appears unrealistic. On the other hand, the data-driven approach produces realistic fingerprints, but it does not guarantee that the resulting fingerprint matches the original fingerprints. In this work, we introduce an algorithm that combines minutiae-oriented and GAN-based approaches to generate morphed fingerprints that look realistic and match their original fingerprints. The algorithm is initially designed to generate double-identity fingerprints and is further extended to generate triple-identity fingerprints. The results of our experiments indicate that the generated fingerprints appear realistic and the majority of them can be seen as double-identity fingerprints. The fingerprints resulting from morphing three fingerprints are unlikely to be triple-identity fingerprints, but rather anonymous ones matching none of the constituent original fingerprints.

In this paper we explore the power of conditional generative adversarial networks and in particular of the pix2pix network to reconstruct realistic fingerprint patterns from minutiae maps. In our considerations a minutiae map is a grayscale image that encodes minutiae locations and orientations as these are presented in a minutiae template. We propose a novel approach for minutiae encoding in a minutiae map and study to which degree the reconstruction may be successful if trained with a low number of samples. Moreover, we explore the generalization ability of the trained models in cross-dataset and cross-sensor experiments. Reconstruction from pseudo-random minutiae enables synthesis of anonymous fingerprints as well as controlling the diversity of generated samples including synthesis of mated fingerprints which is vital for compilation of large-scale public evaluation datasets.

Generation of synthetic biometric samples such as, for instance, fingerprint images gains more and more importance especially in view of recent cross-border regulations on security of private data. The reason is that biometric data is designated in recent regulations such as the EU GDPR as a special category of private data, making sharing datasets of biometric samples hardly possible even for research purposes. The usage of fingerprint images in forensic research faces the same challenge. The replacement of real datasets by synthetic datasets is the most advantageous straightforward solution which bears, however, the risk of generating "unrealistic" samples or "unrealistic distributions" of samples which may visually appear realistic. Despite numerous efforts to generate high-quality fingerprints, there is still no common agreement on how to define "high-quality'' and how to validate that generated samples are realistic enough. Here, we propose general requirements on synthetic biometric samples (that are also applicable for fingerprint images used in forensic application scenarios) together with formal metrics to validate whether the requirements are fulfilled. Validation of our proposed requirements enables establishing the quality of a generative model (informed evaluation) or even the quality of a dataset of generated samples (blind evaluation). Moreover, we demonstrate in an example how our proposed evaluation concept can be applied to a comparison of real and synthetic datasets aiming at revealing if the synthetic samples exhibit significantly different properties as compared to real ones.

Morphing of two fingerprints is shown to be feasible when using a model-based minutia-oriented approach, in which original fingerprint images are cut to two almost equal parts. The morphed fingerprint is a result of assembling two parts of different fingerprints along a cut line. It is important that each part of an original fingerprint in the morphed fingerprint contains enough minutiae to enable the successful matching between the morphed and both original fingerprints. The major drawback of this approach is that the resulting fingerprint often does not appear realistic. Another way to morph fingerprints is exploiting neural generative models. The projections of fingerprints onto the latent space of the generator network are blended and the resulting latent vector is fed to the generator network. In contrast to the model-based approach, a morphed fingerprint almost always appears realistic, but there is no guarantee that it matches successfully both original fingerprints, unless the identity prior is included into the generation process. This paper discusses the advantages and pitfalls of fingerprint morphing using generative adversarial networks (GAN). We experimentally show that GAN-based fingerprint morphing is feasible for creating double-identity fingerprints but fails to anonymize fingerprints i.e. create new virtual identities.

Abstract Information fusion, i.e., the combination of expert systems, has a huge potential to improve the accuracy of pattern recognition systems. During the last decades, various application fields started to use different fusion concepts extensively. The forensic sciences are still hesitant if it comes to blindly applying information fusion. Here, a potentially negative impact on the classification accuracy, if wrongly used or parameterized, as well as the increased complexity (and the inherently higher costs for plausibility validation) of fusion is in conflict with the fundamental requirements for forensics. The goals of this paper are to explain the reasons for this reluctance to accept such a potentially very beneficial technique and to illustrate the practical issues arising when applying fusion. For those practical discussions the exemplary application scenario of morphing attack detection (MAD) is selected with the goal to facilitate the understanding between the media forensics community and forensic practitioners. As general contributions, it is illustrated why the naive assumption that fusion would make the detection more reliable can fail in practice, i.e., why fusion behaves in a field application sometimes differently than in the lab. As a result, the constraints and limitations of the application of fusion are discussed and its impact to (media) forensics is reflected upon. As technical contributions, the current state of the art of MAD is expanded by: The introduction of the likelihood-based fusion and an fusion ensemble composition experiment to extend the set of methods (majority voting, sum-rule, and Dempster-Shafer Theory of evidence) used previously The direct comparison of the two evaluation scenarios “MAD in document issuing” and “MAD in identity verification” using a realistic and some less restrictive evaluation setups A thorough analysis and discussion of the detection performance issues and the reasons why fusion in a majority of the test cases discussed here leads to worse classification accuracy than the best individual classifier

A morphed face image injected into an identity document destroys the unique link between a person and a document meaning that such a multi-identity document may be successfully used by several persons for face-recognition-based identity verification. A morphed face in an electronic machine readable travel document may allow a wanted criminal to illicitly cross a border. This paper describes an improvement of our ongoing web-based experiment for a border control simulation in which human examiners should first detect high-resolution morphed face images and second match potentially morphed document images against "live" faces of travelers. The error rates of humans in both parts of the experiment are compared with those of automated morphing detectors and face recognition systems. This experiment improves understanding the capabilities and limits of humans in withstanding the face morphing attack as well as the factors influencing their performance.

A morphed face image in a photo ID is a serious threat to image-based user verification enabling that multiple persons could be matched with the same document. The application of machine-readable travel documents (MRTD) at automated border control (ABC) gates is an example of a verification scenario that is very sensitive to this kind of fraud. Detection of morphed face images prior to face matching is, therefore, indispensable for effective border security. We introduce the face morphing detection approach based on fitting a logarithmic curve to nine Benford features extracted from quantized DCT coefficients of JPEG compressed original and morphed face images. We separately study the parameters of the logarithmic curve in face and background regions to establish the traces imposed by the morphing process. The evaluation results show that a single parameter of the logarithmic curve may be sufficient to clearly separate morphed and original images.

Since 2014, a novel approach to attack face image based person verification designated as face morphing attack has been actively discussed in the biometric and media forensics communities. Up until that point, modern travel documents were considered to be extremely hard to forge or to successfully manipulate. In the case of template-targeting attacks like facial morphing, the face verification process becomes vulnerable, making it a necessity to design protection mechanisms. In this paper, a new modeling approach for face morphing attacks is introduced. We start with a life-cycle model for photo-ID documents. We extend this model by an image editing history model, allowing for a precise description of attack realizations as a foundation for performing media forensics as well as training and testing scenarios for the attack detectors. On the basis of these modeling approaches, two different realizations of the face morphing attack as well as a forensic morphing detector are implemented and evaluated. The design of the feature space for the detector is based on the idea that the blending operation in the morphing pipeline causes the reduction of face details. To quantify this reduction, we adopt features implemented in the OpenCV image processing library, namely the number of SIFT, SURF, ORB, FAST and AGAST keypoints in the face region as well as the loss of edge-information with Canny and Sobel edge operators. Our morphing detector is trained with 2000 self-acquired authentic and 2000 morphed images captured with three camera types (Canon EOS 1200D, Nikon D 3300, Nikon Coolpix A100) and tested with authentic and morphed face images from a public database. Morphing detection accuracies of a decision tree classifier vary from 81.3% to 98% for different training and test scenarios.

In this paper, we present the main results of the BioSecure Signature Evaluation Campaign (ESRA'2011). The objective of ESRA'2011 is to evaluate through two different tasks the resistance of different online signature systems to skilled forgeries categorized automatically according to their quality. Task 1 aims at studying with only coordinate time functions the influence of acquisition conditions (digitizing tablet vs. PDA) on systems' performance. The two BioSecure Data Sets DS2 and DS3 make this possible, since they contain data from the same 382 people, acquired respectively on a digitizer and on a PDA. Task 2 then aims at assessing the contribution of the five time functions available on a digitizer (coordinates, pressure, pen inclination) on systems' resistance to different qualities of skilled forgeries. Results of the 13 systems involved in this competition are reported and analyzed for both tasks in this paper. We observe that the best system in terms of performance on forgeries of "bad" quality is not necessarily the most resistant to an increased quality of skilled forgeries. Also, we note that mobile conditions are still threatening independently of the quality of forgeries. Finally, when adding pen inclination time functions to pressure and coordinates, we find that the gap between systems in terms of performance is wider than when only pen coordinates and pressure are considered.