P282 - BIOSIG 2018 - Proceedings of the 17th International Conference of the Biometrics Special Interest Group
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- KonferenzbeitragDeep Domain Adaptation for Face Recognition using images captured from surveillance cameras(BIOSIG 2018 - Proceedings of the 17th International Conference of the Biometrics Special Interest Group, 2018) Banerjee, Samik; Bhattacharjee, Avishek; Das, SukhenduLearning based on convolutional neural networks (CNNs) or deep learning has been a major research area with applications in face recognition (FR). However, performances of algorithms designed for FR are unsatisfactory when surveillance conditions severely degrade the test probes. The work presented in this paper has three contributions. First, it proposes a novel adaptive-CNN architecture of deep learning refurbished for domain adaptation (DA), to overcome the difference in feature distributions between the gallery and probe samples. The proposed architecture consists of three components: feature (FM), adaptive (AM) and classification (CM) modules. Secondly, a novel 2-stage algorithm for Mutually Exclusive Training (2-MET) based on stochastic gradient descent, has been proposed. The final stage of training in 2-MET freezes the layers of the FM and CM, while updating (tuning) only the parameters of the AM using a few probe (as target) samples. This helps the proposed deep-DA CNN to bridge the disparities in the distributions of the gallery and probe samples, resulting in enhanced domain-invariant representation for efficient deep-DA learning and classification. The third contribution comes from rigorous experimentations performed on three benchmark real-world surveillance face datasets with various kinds of degradations. This reveals the superior performance of the proposed adaptive-CNN architecture with 2-MET training, using Rank-1 recognition rates and ROC and CMC metrics, over many recent state-of-the-art techniques of CNN and DA.
- KonferenzbeitragFingerprint Presentation Attack Detection using Laser Speckle Contrast Imaging(BIOSIG 2018 - Proceedings of the 17th International Conference of the Biometrics Special Interest Group, 2018) Keilbach, Pascal; Kolberg, Jascha; Gomez-Barrero, Marta; Busch, Christoph; Langweg, HannoWith the increased deployment of biometric authentication systems, some security concerns have also arisen. In particular, presentation attacks directed to the capture device pose a severe threat. In order to prevent them, liveness features such as the blood flow can be utilised to develop presentation attack detection (PAD) mechanisms. In this context, laser speckle contrast imaging (LSCI) is a technology widely used in biomedical applications in order to visualise blood flow. We therefore propose a fingerprint PAD method based on textural information extracted from preprocessed LSCI images. Subsequently, a support vector machine is used for classification. In the experiments conducted on a database comprising 32 different artefacts, the results show that the proposed approach classifies correctly all bona fides. However, the LSCI technology experiences difficulties with thin and transparent overlay attacks.
- KonferenzbeitragBiometric Transaction Authentication using Smartphones(BIOSIG 2018 - Proceedings of the 17th International Conference of the Biometrics Special Interest Group, 2018) Stokkenes, Martin; Ramachandra, Raghavendra; Busch, ChristophSecure and robust authentication of users and customers is critical, as an increasing number of services from banks, health and government sectors are made available to people as online services. Recent development in the area of biometrics, e.g. biometric systems in smartphones, has contributed to higher adoption of the technology as a viable authentication factor in modern systems. In this work, we propose an approach for authenticating transactions in an online bank by using a combination of Bloom filters and error correcting codes. Firstly, protected biometric templates, using Bloom filters, are generated from faces detected in images captured using smartphones. Secondly, a key, shared between a smartphone and a bank server, is encoded using error correcting codes. The encoded key is then secured in the smartphone using the protected biometric templates. Authentication of a banking transaction is realised by unlocking the secured key with a protected biometric template that is close to the template used to lock the key. Experiments are performed on a database consisting of images and videos captured using an iPhone 6S.
- KonferenzbeitragImproved Fingerphoto Verification System Using Multi-scale Second Order Local Structures(BIOSIG 2018 - Proceedings of the 17th International Conference of the Biometrics Special Interest Group, 2018) Wasnik, Pankaj; Ramachandra, Raghavendra; Stokkenes, Martin; Raja, Kiran; Busch, ChristophToday’s high-end smartphones are embedded with advanced fingerprint biometric recognition systems that require dedicated sensors to capture the fingerprint data. The inclusion of such sensors helps in achieving better biometric performance and hence can enable various applications that demand reliable identity verification. However, fingerphoto recognition systems have some inherent advantages over fingerprint recognition such as no latent fingerprints, and it enables the possibility to capture multiple samples at once from a biometric instance with minimal user interaction. Thus, user authentication based on fingerphotos could be a useful alternative as we can re-use the smartphone camera to capture the fingerphotos. On the other hand, such an approach introduces different challenges; for example illumination, orientation, background variation, and focus resulting in lower biometric performance. In this research, we propose a novel verification framework based on the feature extracted from the eigenvalues of convolved images using multi-scale second order Gaussian derivatives. The proposed framework is used to authenticate individuals based on images/ videos of their fingers captured using the built-in smartphone cameras. When combining with the commercial off the shelf (COTS) system, the proposed feature extraction technique has achieved the improved verification performance with an equal error rate of 2:76%.
- KonferenzbeitragFingerprint Quality: a Lifetime Story(BIOSIG 2018 - Proceedings of the 17th International Conference of the Biometrics Special Interest Group, 2018) Galbally, Javier; Haraksim, Rudolf; Beslay, LaurentCurrently, it is a largely accepted fact that biometric sample quality is the most determinant factor to achieve high recognition accuracy in biometric systems. However, even in extensively researched characteristics such as fingerprints, there is still a lack of evidence on how quality evolves throughout the life of an individual. For instance, how does the quality of children fingerprints compare to that of adults or elders? Do these changes imply any age limits for the use of fingerprints with current technology? The present paper addresses this key problem based on a database of over 400K fingerprints coming from more than 250K different fingers. The database was acquired under real operational conditions and contains fingerprints from subjects aged between 0 and 98 years. Such a unique set of data has allowed us to analyse for the first time how fingerprint quality changes through life.
- KonferenzbeitragRobust Clustering-based Segmentation Methods for Fingerprint Recognition(BIOSIG 2018 - Proceedings of the 17th International Conference of the Biometrics Special Interest Group, 2018) Ferreira, Pedro M.; Sequeira, Ana F.; Cardoso, Jaime S.; Rebelo, AnaFingerprint recognition has been widely studied for more than 45 years and yet it remains an intriguing pattern recognition problem. This paper focuses on the foreground mask estimation which is crucial for the accuracy of a fingerprint recognition system. The method consists of a robust cluster-based fingerprint segmentation framework incorporating an additional step to deal with pixels that were rejected as foreground in a decision considered not reliable enough. These rejected pixels are then further analysed for a more accurate classification. The procedure falls in the paradigm of classification with reject option - a viable option in several real world applications of machine learning and pattern recognition, where the cost of misclassifying observations is high. The present work expands a previous method based on the fuzzy C-means clustering with two variations regarding: i) the filters used; and ii) the clustering method for pixel classification as foreground/background. Experimental results demonstrate improved results on FVC datasets comparing with state-of-the-art methods even including methodologies based on deep learning architectures.
- KonferenzbeitragVisible Wavelength Iris Segmentation: A Multi-Class Approach using Fully Convolutional Neuronal Networks(BIOSIG 2018 - Proceedings of the 17th International Conference of the Biometrics Special Interest Group, 2018) Osorio-Roig, Dailé; Rathgeb, Christian; Gomez-Barrero, Marta; Morales-González, Annette; Garea-Llano, Eduardo; Busch, ChristophIris segmentation under visible wavelengths (VWs) is a vital processing step for iris recognition systems operating at-a-distance or in non-cooperative environments. In these scenarios the presence of various artefacts, e.g. occlusions or specular reflections, as well as out-of-focus blur represents a significant challenge. The vast majority of proposed iris segmentation algorithms under VW aim at discriminating the iris and non-iris regions without taking into account the variability that is present in the non-iris region. In this paper, we introduce the idea of segmenting the iris region using a multi-class approach which differentiates additional classes, e.g. pupil or sclera, as opposed to commonly employed bi-class approaches (iris and non-iris). Experimental results conducted on two publicly available databases show that the use of the proposed multi-class approach improves the iris segmentation accuracy. Simultaneously, it also allows for the segmentation of different non-iris regions, e.g. glasses, which could be employed in further application scenarios.
- KonferenzbeitragFEERCI: A Package for Fast Non-Parametric Confidence Intervals for Equal Error Rates in Amortized O(m log n)(BIOSIG 2018 - Proceedings of the 17th International Conference of the Biometrics Special Interest Group, 2018) Haasnoot, Erwin; Khodabakhsh, Ali; Zeinstra, Chris; Spreeuwers, Luuk; Veldhuis, RaymondEqual Error Rates (EERs), or other weighted relations between False Match and Non- Match Rates (FMR/FNMR), are often used as a performance metric for biometric systems. Confidence Intervals (CIs) are used to denote the uncertainty underlying these EERs, with many methods existing to estimate said CIs in both parametric and non-parametric ways. These confidence intervals provide, foremost, a method of comparing scoring/ranking functions. Non-parametric methods often suffer from high computational costs, but do not make assumptions as to the shape of the EERand score distributions. For both EERs and CIs, contemporary open-source toolkits leave room for improvement in terms of computational efficiency. In this paper, we introduce the Fast EER (FEER) algorithm that calculates an EER in O(logn) on a sorted score list, we show how to adapt the FEER algorithm to calculate non-parametric, bootstrapped EER CIs (FEERCI) in O(mlogn) given m resamplings, and we introduce an opinionated open-source package named feerci that provides implementations of the FEER and FEERCI algorithm.We provide speed and accuracy benchmarks for the feerci package, comparing it against the most-used methods of calculating EERs in Python and show how it is able to calculate EERs and CIs on very large score lists faster than contemporary toolkits can calculate a single EER.
- KonferenzbeitragA Novel Mobilephone Application Authentication Approach based on Accelerometer and Gyroscope Data(BIOSIG 2018 - Proceedings of the 17th International Conference of the Biometrics Special Interest Group, 2018) Li, Guoqiang; Bours, PatrickThe advent of mobile phones have changed our daily life. We are heavily relying on various applications installed on mobilephones to communicate with other, to share personal information with them, and to access our bank account, etc. However, the security measurement in terms of accessing these applications is either omitted or user-hostile because of the burden of memorizing the PIN or password. In order to relieve people from such burden, we explore the possibility of developing a mobilephone application authentication approach by analyzing the accelerometer and gyroscope data collected from the first few seconds when the user opens an application. By evaluating several proposed authentication approaches on a dataset collected from a real-life scenario, we achieve the best EER at 22.72% by only using the data collected from first 3 seconds. We think integrating the proposed non-intrusive authentication approach into the mobilephone application as an alternative for PIN/password can provide a more user-friendly authentication mechanism.
- KonferenzbeitragLongitudinal Finger Rotation - Problems and Effects in Finger-Vein Recognition(BIOSIG 2018 - Proceedings of the 17th International Conference of the Biometrics Special Interest Group, 2018) Prommegger, Bernhard; Kauba, Christof; Uhl, AndreasFinger-vein scanners or vein-based biometrics in general are becoming more and more popular. Commercial off-the-shelf finger-vein scanners usually capture only one finger from the palmar side using transillumination. Most scanners have a contact area and a finger-shaped support where the finger has to be placed onto in order to prevent misplacements of the finger including shifts, planar rotation and tilts. However, this is not able to prevent rotation of the finger along its longitudinal axis (also called non-planar finger rotation). This kind of finger rotation poses a severe problem in finger-vein recognition as the resulting vein image may represent entirely different patterns due to the perspective projection. We evaluated the robustness of several finger-vein recognition schemes against longitudinal finger rotation. Therefore, we established a finger-vein data set exhibiting longitudinal finger rotation in steps of 1° covering a range of 90°. Our experimental results confirm that the performance of most of the simple recognition schemes rapidly decreases for more than 10° of rotation, while more advanced schemes are able to handle up to 30°.