P282 - BIOSIG 2018 - Proceedings of the 17th International Conference of the Biometrics Special Interest Group
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- KonferenzbeitragPredicted Templates: Learning-curve Based Template Projection for Keystroke Dynamics(BIOSIG 2018 - Proceedings of the 17th International Conference of the Biometrics Special Interest Group, 2018) Khodabakhsh, Ali; Haasnoot, Erwin; Bours, PatrickKeystroke Dynamics (KD) as a biometric modality can provide authentication tools in many real-life applications, virtually at zero-cost on the client side, due to the reliance of these techniques on existing hardware, and their low computational expense. One promising application is the use of KD as a second factor in password-based authentication. A downside of the existing modeling methods is the assumption of stationary behavior from the clients. However, it is expected that humans show improvements in performing a specific task following practice. In this study, we propose methods for utilization of learning models in predicting the future behavior of the clients, even with little enrollment data, and generate predicted behavioral models that can be used in different classifiers. In our experiments, the predicted templates show a reduction in the average equal-error-rate (EER) consistently across different classifiers a benchmark dataset. A reduction of 20% is achieved on the best classifier. Given fewer enrollment data, the performance gain was shown to reach above 30%. Furthermore, we show that blind detection of attacks is possible, solely relying on the global learning curve, with an EER of 16%.
- KonferenzbeitragShallow CNNs for the Reliable Detection of Facial Marks(BIOSIG 2018 - Proceedings of the 17th International Conference of the Biometrics Special Interest Group, 2018) Zeinstra, Chris; Haasnoot, ErwinFacial marks are local irregularities of skin texture. Their type and/or spatial pattern can be used as a (soft) biometric modality in several applications. A key requirement for a biometric system that utilises facial marks is their reliable detection. Detection methods typically use a blob detector followed by heuristic post processing steps to reduce the number of false positives. In this paper, we consider shallow Convolutional Neural Networks (CNNs) for facial mark detection. The choice of this network type seems natural as it learns multiple (non) blob detectors; shallow refers to the fact that we only consider CNNs up to three layers.We show that (a) these CNNs successfully address the false positive problem, (b) remove the need for post processing steps, and (c) outperform a classic blob detector, approaches taken in previous studies and some other non CNN type classifiers in terms of EER and FMR at TMR=0.95.
- KonferenzbeitragUnsupervised Learning of Fingerprint Rotations(BIOSIG 2018 - Proceedings of the 17th International Conference of the Biometrics Special Interest Group, 2018) Schuch, Patrick; May, Jan Marek; Busch, ChristophThe alignment of fingerprint samples is a preprocessing step in fingerprint recognition. It allows an improved biometric feature extraction and a more accurate biometric comparison. We propose to use Convolutional Neural Networks for estimation of the rotational part. The main contribution is an unsupervised training strategy similar to Siamese Networks for estimation of rotations. The approach does not need any labelled data for training. It is trained to estimate orientation differences for pairs of samples. Our approach achieves an alignment accuracy with a mean absolute deviation 2:1 on data similar to the training data, which supports the alignment task. For other datasets accuracies down to 6:2 mean absolute deviation are achieved.
- KonferenzbeitragPeriocular Recognition Using CNN Features Off-the-Shelf(BIOSIG 2018 - Proceedings of the 17th International Conference of the Biometrics Special Interest Group, 2018) Hernandez-Diaz, Kevin; Alonso-Fernandez, Fernando; Bigun, JosefPeriocular refers to the region around the eye, including sclera, eyelids, lashes, brows and skin. With a surprisingly high discrimination ability, it is the ocular modality requiring the least constrained acquisition. Here, we apply existing pre-trained architectures, proposed in the context of the ImageNet Large Scale Visual Recognition Challenge, to the task of periocular recognition. These have proven to be very successful for many other computer vision tasks apart from the detection and classification tasks for which they were designed. Experiments are done with a database of periocular images captured with a digital camera. We demonstrate that these off-the-shelf CNN features can effectively recognize individuals based on periocular images, despite being trained to classify generic objects. Compared against reference periocular features, they show an EER reduction of up to 40%, with the fusion of CNN and traditional features providing additional improvements.
- KonferenzbeitragJekyll and Hyde: On The Double-Faced Nature of Smart-Phone Sensor Noise Injection(BIOSIG 2018 - Proceedings of the 17th International Conference of the Biometrics Special Interest Group, 2018) Matovu, Richard; Serwadda, Abdul; Irakiza, David; Griswold-Steiner, IsaacTo combat privacy attacks that exploit the motion and orientation sensors embedded in mobile devices, a number of recent works have proposed noise injection schemes that degrade the quality of sensor data. Much as these schemes have been shown to thwart the attacks, the impact of noise injection on continuous authentication schemes proposed for mobile and wearable devices has never been studied. In this paper, we empirically tackle this question based on two widely studied continuous authentication applications (i.e., gait and handwriting authentication). Through a series of machine learning and statistical techniques, we show that the thresholds of noise needed to overcome the attacks would significantly degrade the performance of the continuous authentication applications. The paper argues against noise injection as a defense against attacks that exploit motion and orientation sensor data on mobile and wearable devices.
- KonferenzbeitragAdvanced Face Presentation Attack Detection on Light Field Database(BIOSIG 2018 - Proceedings of the 17th International Conference of the Biometrics Special Interest Group, 2018) Chiesa, Valeria; Dugelay, Jean-LucIn the last years several works have been focused on the impact of new sensors on face recognition. A particular interest has been addressed to technologies able to detect the depth of the scene as light field cameras. Together with person identification algorithms, new anti-spoofing methods customized for specific devices have to be investigated. In this paper, a new algorithm for presentation attack detection on light field face database is proposed. While distance between subject and camera is not a relevant information for standard 2D spoofing attacks, it could be important when using 3D cameras. We prove through three experiments that the proposed method based on depth map elaboration outperforms the existent algorithms in presentation attack detection on light field images.
- KonferenzbeitragA benchmark database of visible and thermal paired face images across multiple variations(BIOSIG 2018 - Proceedings of the 17th International Conference of the Biometrics Special Interest Group, 2018) Mallat, Khawla; Dugelay, Jean-LucAlthough visible face recognition systems have grown as a major area of research, they are still facing serious challenges when operating in uncontrolled environments. In attempt to overcome these limitations, thermal imagery has been investigated as a promising direction to extend face recognition technology. However, the reduced number of databases acquired in thermal spectrum limits its exploration. In this paper, we introduce a database of face images acquired simultaneously in visible and thermal spectra under various variations: illumination, expression, pose and occlusion. Then, we present a comparative study of face recognition performances on both modalities against each variation and the impact of bimodal fusion. We prove that thermal spectrum rivals with the visible spectrum not only in the presence of illumination changes, but also in case of expression and poses changes.
- KonferenzbeitragEstimating the Data Origin of Fingerprint Samples(BIOSIG 2018 - Proceedings of the 17th International Conference of the Biometrics Special Interest Group, 2018) Schuch, Patrick; May, Jan Marek; Busch, ChristophThe data origin (i.e. acquisition technique and acquisition mode) can have a significant impact on the appearance and characteristics of a fingerprint sample. This dataset bias might be challenging for processes like biometric feature extraction. Much effort can be put into data normalization or into processes able to deal with almost any input data. The performance of the former might suffer from this general applicability. The latter losses information by definition. If one is able to reliably identify the data origin of fingerprints, one will be able to dispatch the samples to specialized processes. Six methods of classification are evaluated for their capabilities to distinguish between fifteen different datasets. Acquisition technique and acquisition mode can be classified very accurately. Also, most of the datasets can be distinguished reliably.
- KonferenzbeitragFake Face Detection Methods: Can They Be Generalized?(BIOSIG 2018 - Proceedings of the 17th International Conference of the Biometrics Special Interest Group, 2018) Khodabakhsh, Ali; Ramachandra, Raghavendra; Raja, Kiran; Wasnik, Pankaj; Busch, ChristophWith advancements in technology, it is now possible to create representations of human faces in a seamless manner for fake media, leveraging the large-scale availability of videos. These fake faces can be used to conduct personation attacks on the targeted subjects. Availability of open source software and a variety of commercial applications provides an opportunity to generate fake videos of a particular target subject in a number of ways. In this article, we evaluate the generalizability of the fake face detection methods through a series of studies to benchmark the detection accuracy. To this extent, we have collected a new database of more than 53;000 images, from 150 videos, originating from multiple sources of digitally generated fakes including Computer Graphics Image (CGI) generation and many tampering based approaches. In addition, we have also included images (with more than 3;200) from the predominantly used Swap-Face application that is commonly available on smart-phones. Extensive experiments are carried out using both texture-based handcrafted detection methods and deep learning based detection methods to find the suitability of detection methods. Through the set of evaluation, we attempt to answer if the current fake face detection methods can be generalizable.
- KonferenzbeitragBenefits of Gaussian Convolution in Gait Recognition(BIOSIG 2018 - Proceedings of the 17th International Conference of the Biometrics Special Interest Group, 2018) Marsico, Maria De; Mecca, AlessioThe first and still popular approach to gait recognition applies computer vision techniques to appearance-based features of walking patterns. More recently, wearable sensors have become attractive. The accelerometer is the most used one, being embedded in widespread mobile devices. Related techniques do not suffer for problems like occlusion and point of view, but for intra-subject variations caused by walking speed, ground type, shoes, etc. However, we can often recognize a person from the walking pattern, and this stimulates to search for robust features, able to sufficiently characterize this trait. This paper presents some preliminary experiments using the convolution with Gaussian kernels to extract relevant gait elements. The experiments use the large ZJU-gaitacc public dataset, and achieve improved results compared with previous works exploiting the same dataset.