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P315 - BIOSIG 2021 - Proceedings of the 20th International Conference of the Biometrics Special Interest Group

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  • Konferenzbeitrag
    Emerging biometric modalities and their use: Loopholes in the terminology of the GDPR and resulting privacy risks
    (BIOSIG 2021 - Proceedings of the 20th International Conference of the Biometrics Special Interest Group, 2021) Bisztray, Tamás; Gruschka, Nils; Bourlai, Thirimachos; Fritsch, Lothar
    Technological advancements allow biometric applications to be more omnipresent than in any other time before. This paper argues that in the current EU data protection regulation, classification applications using biometric data receive less protection compared to biometric recognition. We analyse preconditions in the regulatory language and explore how this has the potential to be the source of unique privacy risks for processing operations classifying individuals based on soft traits like emotions. This can have high impact on personal freedoms and human rights and, therefore, should be subject to data protection impact assessment.
  • Konferenzbeitrag
    Towards Generating High Definition Face Images from Deep Templates
    (BIOSIG 2021 - Proceedings of the 20th International Conference of the Biometrics Special Interest Group, 2021) Dong, Xingbo; Jin, Zhe; Guo, Zhenhua; Teoh, Andrew Beng Jin
    Face recognition based on deep convolutional neural networks (CNN) has manifested superior accuracy. Despite the high discriminability of deep features generated by CNN, the vulnerability of the deep feature is often overlooked and leads to security and privacy concerns, particularly, the risks of reconstructing face images from the deep templates. In this paper, we propose a method to generate high definition (HD) face images from deep features. To be specific, the deep features extracted from CNN are mapped to the input (latent vector) of the pre-trained StyleGAN2 using a regression model. Subsequently, HD face images can be generated based on the latent vector by the pre-trained StyleGAN2 model. To evaluate our method, we derived the face features from the generated HD face images and compared against the bona fide face features. In the sense of face image reconstruction, our method is simple, yet the experimental results suggest the effectiveness, which achieves an attack performance as high as TAR=46.08% (18.30%) @ FAR=0.1 threshold under type-I (type-II) attack settings. Besides, experiment results also indicate that 50.7% of generated HD face images can pass one commercial off-the-shelf (COTS) liveness detection.
  • Konferenzbeitrag
    Gait Authentication based on Spiking Neural Networks
    (BIOSIG 2021 - Proceedings of the 20th International Conference of the Biometrics Special Interest Group, 2021) Rúa, Enrique Argones; van Hamme, Tim; Preuveneers, Davy; Joosen, Wouter
    In this paper we address gait authentication using a novel approach based on spiking neural networks (SNNs). This technology has proven advantages regarding energy consumption and it is a perfect match with some proposed neuromorphic hardware chips, which can lead to a broader adoption of user device applications of artificial intelligence technologies. One of the challenges when using this technology is the training of the network itself, since it is not straightforward to apply well-known error backpropagation, massively used in traditional artificial neural networks (ANNs). In this paper we propose a new derivation of error backpropagation for the spiking neural networks that integrates lateral inhibition and provides competitive results when compared to state of the art ANNs in the context of IMU-based gait authentication.
  • Konferenzbeitrag
    Shuffled Patch-Wise Supervision for Presentation Attack Detection
    (BIOSIG 2021 - Proceedings of the 20th International Conference of the Biometrics Special Interest Group, 2021) Kantarcı, Alperen; Dertli, Hasan; Ekenel, Hazım Kemal
    Face anti-spoofing is essential to prevent false facial verification by using a photo, video, mask, or a different substitute for an authorized person's face. Most of the state-of-the-art presentation attack detection (PAD) systems suffer from overfitting, where they achieve near-perfect scores on a single dataset but fail on a different dataset with more realistic data. This problem drives researchers to develop models that perform well under real-world conditions. This is an especially challenging problem for frame-based presentation attack detection systems that use convolutional neural networks (CNN). To this end, we propose a new PAD approach, which combines pixel-wise binary supervision with patch-based CNN. We believe that training a CNN with face patches allows the model to distinguish spoofs without learning background or dataset-specific traces. We tested the proposed method both on the standard benchmark datasets ---Replay-Mobile, OULU-NPU--- and on a real-world dataset. The proposed approach shows its superiority on challenging experimental setups. Namely, it achieves higher performance on OULU-NPU protocol 3, 4 and on inter-dataset real-world experiments.
  • Konferenzbeitrag
    Interoperability of Contact and Contactless Fingerprints Across Multiple Fingerprint Sensors
    (BIOSIG 2021 - Proceedings of the 20th International Conference of the Biometrics Special Interest Group, 2021) Williams, Brady; McCauley, John; Dando, John; Nasrabadi, Nasser; Dawson, Jeremy
    Contactless fingerprinting devices have grown in popularity in recent years due to speed and convenience of capture. Also, due to the global COVID-19 pandemic, the need for safe and hygienic options for fingerprint capture are more pressing than ever. However, contactless systems face challenges in the areas of interoperability and matching performance as shown in other works. In this paper, we present a contactless vs. contact interoperability assessment of several contactless devices, including cellphone fingerphoto capture. In addition to evaluating the match performance of each contactless sensor, this paper presents an analysis of the impact of finger size and skin melanin content on contactless match performance. AUC results indicate that contactless match performance of the newest contactless devices is reaching that of contact fingerprints. In addition, match scores indicate that, while not as sensitive to melanin content, contactless fingerprint matching may be impacted by finger size.
  • Konferenzbeitrag
    Curricular SincNet: Towards Robust Deep Speaker Recognition by Emphasizing Hard Samples in Latent Space
    (BIOSIG 2021 - Proceedings of the 20th International Conference of the Biometrics Special Interest Group, 2021) Chowdhury, Labib; Kamal, Mustafa; Tasnim, Najia; Mohammed, Nabeel
    Deep learning models have become an increasingly preferred option for biometric recognition systems; such as speaker recognition. SincNet, a deep neural network architecture gained popularity in speaker recognition tasks, due to its use of parameterized sinc functions that allow it to work directly on the speech signal. The original SincNet architecture uses the softmax loss which may not be the most suitable choice for recognition-based tasks, as such loss functions do not impose inter-class margins nor does it differentiate between easy and hard training samples. Curriculum learning, particularly those leveraging angular margin-based losses has proven to be very successful in other biometric applications such as face recognition. The advantage of such a curriculum learning-based techniques is that it will impose inter-class margins as well as taking to account easy and hard samples. In this paper, we propose Curricular SincNet (CL-SincNet), an improved SincNet model where we use a curricular loss function to do the training on the SincNet architecture. The proposed model is evaluated on multiple datasets using intra-dataset and inter-dataset evaluation protocol. In both settings, the model performs competitively with other previously published work and in the case of inter-dataset testing, it achieves the best overall results with a reduction of 4% error rate compare to SincNet and other published work.
  • Konferenzbeitrag
    BIOSIG 2021 - Complete Volume
    (BIOSIG 2021 - Proceedings of the 20th International Conference of the Biometrics Special Interest Group, 2021)
  • Konferenzbeitrag
    Assessment of Sensor Ageing-Impact in Air Travelled Fingerprint Capturing Devices
    (BIOSIG 2021 - Proceedings of the 20th International Conference of the Biometrics Special Interest Group, 2021) Kauba, Christof; Kirchgasser, Simon; Jöchl, Robert; Uhl, Andreas
    Biometric recognition performance is affected by many factors, like varying acquisition conditions or ageing related effects, commonly denoted as biometric template ageing. Image sensor ageing, being part of biometric template ageing and a sub-field of image and video forensics, leads to defective pixels due to cosmic radiation, depending on the altitude. So far, image sensor ageing has only been a peripheral target in fingerprint research. We investigate the impact of image sensor ageing on various fingerprint capturing devices, including optical, capacitive and thermal ones. We established a fingerprint ageing dataset utilising 10 capturing devices which travelled on an air-plane for 127 days (to increase the number of developed defects). By evaluating the samples captured prior to their travel and afterwards using several state-of-the-art fingerprint quality metrics as well as minutiae-based fingerprint recognition systems we quantify the effect of image sensor ageing on fingerprint recognition. Furthermore, by employing a defect detection technique we quantify the number of defects developed during that period.
  • Konferenzbeitrag
    Rotation Tolerant Finger Vein Recognition using CNNs
    (BIOSIG 2021 - Proceedings of the 20th International Conference of the Biometrics Special Interest Group, 2021) Promegger, Bernhard; Wimmer, Georg; Uhl, Andreas
    Finger vein recognition deals with the recognition of subjects based on their venous pattern within the fingers. The majority of the available systems acquire the vein pattern using only a single camera. Such systems are susceptible to misplacements of the finger during acquisition, in particular longitudinal finger rotation poses a severe problem. Besides some hardware based approaches that try to avoid the misplacement in the first place, there are several software based solutions to counter fight longitudinal finger rotation. All of them use classical hand-crafted features. This work presents a novel approach to make CNNs robust to longitudinal finger rotation by training CNNs using finger vein images from varying perspectives.
  • Konferenzbeitrag
    N-shot Palm Vein Verification Using Siamese Networks
    (BIOSIG 2021 - Proceedings of the 20th International Conference of the Biometrics Special Interest Group, 2021) Marattukalam, Felix; Abdulla, Waleed H.; Swain, Akshya
    The use of deep learning methods to extract vascular biometric patterns from the palm surface has been of interest among researchers in recent years. In many biometric recognition tasks, there is a limit in the number of training samples. This is because of limited vein biometric databases being available for research. This restricts the application of deep learning methods to design algorithms that can effectively identify or authenticate people for vein recognition. This paper proposes an architecture using Siamese neural network structure for few shot palm vein verification. The proposed network uses images from both the palms and consists of two sub-nets that share weights to identify a person. The architecture’s performance was tested on the HK PolyU multi spectral palm vein database with limited samples. The results suggest that the method is effective since it has 91.9% precision, 91.1% recall, 92.2% specificity, 91.5% F1-Score, and 90.5% accuracy values.