P306 - BIOSIG 2020 - Proceedings of the 19th International Conference of the Biometrics Special Interest Group
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- KonferenzbeitragA robust fingerprint presentation attack detection method against unseen attacks through adversarial learning(BIOSIG 2020 - Proceedings of the 19th International Conference of the Biometrics Special Interest Group, 2020) Pereira, Joao Afonso; Sequeira, Ana F.; Pernes, Diogo; Cardoso, Jaime S.Fingerprint presentation attack detection (PAD) methods present a stunning performance in current literature. However, the fingerprint PAD generalisation problem is still an open challenge requiring the development of methods able to cope with sophisticated and unseen attacks as our eventual intruders become more capable. This work addresses this problem by applying a regularisation technique based on an adversarial training and representation learning specifically designed to to improve the PAD generalisation capacity of the model to an unseen attack. In the adopted approach, the model jointly learns the representation and the classifier from the data, while explicitly imposing invariance in the high-level representations regarding the type of attacks for a robust PAD. The application of the adversarial training methodology is evaluated in two different scenarios: i) a handcrafted feature extraction method combined with a Multilayer Perceptron (MLP); and ii) an end-to-end solution using a Convolutional Neural Network (CNN). The experimental results demonstrated that the adopted regularisation strategies equipped the neural networks with increased PAD robustness. The adversarial approach particularly improved the CNN models’ capacity for attacks detection in the unseen-attack scenario, showing remarkable improved APCER error rates when compared to state-of-the-art methods in similar conditions.
- KonferenzbeitragEyebrow Deserves Attention: Upper Periocular Biometrics(BIOSIG 2020 - Proceedings of the 19th International Conference of the Biometrics Special Interest Group, 2020) Nguyen, Hoang (Mark); Rattani, V; Derakhshani, RezaOcular biometrics is attracting exceeding attention from research community and industry alike thanks to its accuracy, security, and ease of use in mobile devices, especially in the presence of occlusions such as masks worn during the COVID-19 pandemic. When considering the extended periocular region, eyebrows have not been getting enough attention due to their perceived low uniqueness. In this paper, we evaluate a mobile-friendly deep-learning model for eyebrow-based user authentication. Specifically, we used a fine-tuned lightCNN model for eyebrow based user authentication with promising results on a particularly challenging dataset and evaluation protocol (open-set with simulated twins). The methods achieved 0:99 AUC and 4:3% EER in VISOB dataset and 0:98 AUC and 5:6% EER on SiW datasets using closed-set and open-set analysis, respectively.
- KonferenzbeitragOn the assessment of face image quality based on handcrafted features(BIOSIG 2020 - Proceedings of the 19th International Conference of the Biometrics Special Interest Group, 2020) Henniger, Olaf; Fu, Biying; Chen, CongThis paper studies the assessment of the quality of face images, predicting the utility of face images for automated recognition. The utility of frontal face images from a publicly available dataset was assessed by comparing them with each other using commercial off-the-shelf face recognition systems. Multiple face image features delineating face symmetry and characteristics of the capture process were analysed to find features predictive of utility. The selected features were used to build system-specific and generic random forest classifiers.
- KonferenzbeitragPrivacy Evaluation Protocols for the Evaluation of Soft-Biometric Privacy-Enhancing Technologies(BIOSIG 2020 - Proceedings of the 19th International Conference of the Biometrics Special Interest Group, 2020) Terhörst, Philipp; Huber, Marco; Damer, Naser; Rot, Peter; Kirchbuchner, Florian; Struc, Vitomir; Kuijper, ArjanBiometric data includes privacy-sensitive information, such as soft-biometrics. Soft-biometric privacy enhancing technologies aim at limiting the possibility of deducing such information. Previous works proposed several solutions to this problem using several different evaluation processes, metrics, and attack scenarios. The absence of a standardized evaluation protocol makes a meaningful comparison of these solutions difficult. In this work, we propose privacy evaluation protocols (PEPs) for privacy-enhancing technologies (PETs) dealing with soft-biometric privacy. Our framework evaluates PETs in the most critical scenario of an attacker that knows and adapts to the systems privacy-mechanism. Moreover, our PEPs differentiate between PET of learning-based or training-free nature. To ensure that our protocol meets the highest standards in both cases, it is based on Kerckhoffs‘s principle of cryptography.
- KonferenzbeitragTouchless Fingerprint Sample Quality: Prerequisites for the Applicability of NFIQ2.0(BIOSIG 2020 - Proceedings of the 19th International Conference of the Biometrics Special Interest Group, 2020) Priesnitz, Jannis; Rathgeb, Christian; Buchmann, Nicolas; Busch, ChristophThe impact of fingerprint sample quality on biometric performance is undisputed. For touch-based fingerprint data, the effectiveness of the NFIQ2.0 quality estimation method is well documented in scientific literature. Due to the increasing use of touchless fingerprint recognition systems a thorough investigation of the usefulness of the NFIQ2.0 for touchless fingerprint data is of interest. In this work, we investigate whether NFIQ2.0 quality scores are predictive of error rates associated with the biometric performance of touchless fingerprint recognition. For this purpose, we propose a touchless fingerprint preprocessing that favours NFIQ2.0 quality estimation which has been designed for touch-based fingerprint data. Comparisons are made between NFIQ2.0 score distributions obtained from touch-based and touchless fingerprint data of the publicly available FVC06, MCYT, PolyU, and ISPFDv1 databases. Further, the predictive power regarding biometric performance is evaluated in terms of Error-versus-Reject Curves (ERCs) using an open source fingerprint recognition system. Under constrained capture conditions NFIQ2.0 is found to be an effective tool for touchless fingerprint quality estimation if an adequate preprocessing is applied.
- KonferenzbeitragImproved Liveness Detection in Dorsal Hand Vein Videos using Photoplethysmography(BIOSIG 2020 - Proceedings of the 19th International Conference of the Biometrics Special Interest Group, 2020) Schuiki, Johannes; Uhl, AndreasIn this study, a previously published infrared finger vein liveness detection scheme is tested for its applicability on dorsal hand vein videos. A custom database consisting of five different types of presentation attacks recorded with transillumination as well as reflected light illumination is examined. Additionally, two different methods for liveness detection are presented in this work. All methods described employ the concept of generating a signal through the change in average pixel illumination, which is referred to as Photoplethysmography. Feature vectors in order to classify a given video sequence are generated using spectral analysis of the time series. Experimental results show the effectiveness of the proposed methods.
- KonferenzbeitragWatchlist Adaptation: Protecting the Innocent(BIOSIG 2020 - Proceedings of the 19th International Conference of the Biometrics Special Interest Group, 2020) Günther, Manuel; Dhamija, Akshay Raj; Boult, Terrance E.One of the most important government applications of face recognition is the watchlist problem, where the goal is to identify a few people enlisted on a watchlist while ignoring the majority of innocent passersby. Since watchlists dynamically change and training times can be expensive, the deployed approaches use pre-trained deep networks only to provide deep features for face comparison. Since these networks never specifically trained on the operational setting or faces from the watchlist, the system will often confuse them with the faces of innocent non-watchlist subjects leading to difficult situations, e.g., being detained at the airport to resolve their identity. We develop a novel approach to take an existing pre-trained face network and use adaptation layers trained with our recently developed Objectosphere loss to provide an open-set recognition system that is rapidly adapted to the gallery while also ignoring non-watchlist faces as well as any background detections from the face detector. While our adapter network can be quickly trained without the need of re-training the entire representation network, it can also significantly improve the performance of any state-of-the-art face recognition network like VGG2. We experiment with the largest open-set face recognition dataset, the UnConstrained College Students (UCCS). It contains real surveillance camera stills including both known and unknown subjects, as well as many non-face regions from the face detector. We show that the Objectosphere approach is able to reduce the feature magnitude of unknown subjects as well as background detections, so that we can apply a specifically designed similarity function on the deep features of the Objectosphere network, which works much better than the direct prediction of the very same network. Additionally, our approach outperforms the VGG2 baseline by a large margin by rejecting the non-face data, and also outperforms prior state-of-the-art open-set recognition algorithms on the VGG2 baseline data.
- KonferenzbeitragA Generalizable Deepfake Detector based on Neural Conditional Distribution Modelling(BIOSIG 2020 - Proceedings of the 19th International Conference of the Biometrics Special Interest Group, 2020) Khodabakhsh, Ali; Busch, ChristophPhoto- and video-realistic generation techniques have become a reality following the advent of deep neural networks. Consequently, there are immense concerns regarding the difficulty in differentiating what content is real from what is synthetic. An example of video-realistic generation techniques is the infamous Deepfakes, which exploit the main modality by which humans identify each other. Deepfakes are a category of synthetic face generation methods and are commonly based on generative adversarial networks. In this article, we propose a novel two-step synthetic face image detection method in which general-purpose features are extracted in a first step, trivializing the task of detecting synthetic images. The anomaly detector predicts the conditional probabilities for observing every individual pixel in the image and is trained on pristine data only. The extracted anomaly features demonstrate true generalization capacity across widely different unknown synthesis methods while showing a minimal loss in performance with regard to the detection of known synthetic samples.
- KonferenzbeitragIris Recognition in Postmortem Enucleated Eyes(BIOSIG 2020 - Proceedings of the 19th International Conference of the Biometrics Special Interest Group, 2020) Saripalle, Sashi K.; McLaughlin, Adam; Derakhshani, RezaThis paper presents a comprehensive multispectral study of iris recognition on postmortem enucleated eyes over a period of three days. An off the shelf iris recognition methodology is employed to analyze the biometric capability of iris in the post mortem setting.We observed that iris patterns of enucleated eyes can provide biometric matches with no false accepts for up to 164 hours after death, albeit with high false rejection rates. We also present our observations on the effects of the environment and other confounding factors that may affect the performance of postmortem iris recognition, with recommendations for rehydration of specimen to regain postmortem biometric utility.
- KonferenzbeitragExplaining ECG Biometrics: Is It All In The QRS?(BIOSIG 2020 - Proceedings of the 19th International Conference of the Biometrics Special Interest Group, 2020) Pinto, João Ribeiro; Cardoso, Jaime S.The literature seems to indicate that the QRS complex is the most important component of the electrocardiogram (ECG) for biometrics. To verify this claim, we use interpretability tools to explain how a convolutional neural network uses ECG signals to identify people, using on-theperson (PTB) and off-the-person (UofTDB) signals. While the QRS complex appears indeed to be a key feature on ECG biometrics, especially with cleaner signals, results indicate that, for larger populations in off-the-person settings, the QRS shares relevance with other heartbeat components, which it is essential to locate. These insights indicate that avoiding excessive focus on the QRS complex, using decision explanations during training, could be useful for model regularisation.