P315 - BIOSIG 2021 - Proceedings of the 20th International Conference of the Biometrics Special Interest Group
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- KonferenzbeitragN-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, AkshyaThe 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.
- KonferenzbeitragAssessment 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, AndreasBiometric 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.
- KonferenzbeitragOn the Relevance of Minutiae Count and Distribution for Finger Vein Recognition Accuracy(BIOSIG 2021 - Proceedings of the 20th International Conference of the Biometrics Special Interest Group, 2021) Linortner, Michael; Uhl, AndreasVein recognition usually uses binary features, but besides deep learning-based approaches key-point and minutiae-based ones started to become popular as well. Statistical measures for vein minutiae points, like spatial point distribution, have not been investigated in literature so far. In this work the number of vein minutiae points and their spatial distribution is analyzed in relation to recognition accuracy. The goal is to initiate a discussion on statistical behavior of vein minutiae points and deriving possible quality measures for vein minutiae point sets.
- KonferenzbeitragOn Brightness Agnostic Adversarial Examples Against Face Recognition Systems(BIOSIG 2021 - Proceedings of the 20th International Conference of the Biometrics Special Interest Group, 2021) Singh, Inderjeet; Momiyama, Satoru; Kakizaki, Kazuya; Araki, ToshinoriThis paper introduces a novel adversarial example generation method against face recognition systems (FRSs). An adversarial example (AX) is an image with deliberately crafted noise to cause incorrect predictions by a target system. The AXs generated from our method remain robust under real-world brightness changes. Our method performs non-linear brightness transformations while leveraging the concept of curriculum learning during the attack generation procedure. We demonstrate that our method outperforms conventional techniques from comprehensive experimental investigations in the digital and physical world. Furthermore, this method enables practical risk assessment of FRSs against brightness agnostic AXs.
- KonferenzbeitragVein Enhancement with Deep Auto-Encoders to improve Finger Vein Recognition(BIOSIG 2021 - Proceedings of the 20th International Conference of the Biometrics Special Interest Group, 2021) Bros, Victor; Kotwal, Ketan; Marcel, SébastienThe field of Vascular Biometric Recognition has drawn a lot of attention recently with the emergence of new computer vision techniques. The different methods using Deep Learning involve a new understanding of deeper features from the vascular network. The specific architecture of the veins needs complex model capable of comprehending the vascular pattern. In this paper, we present an image enhancement method using Deep Convolutional Neural Network. For this task, a residual convolutional auto-encoder architecture has been trained in a supervised way to enhance the vein patterns in near-infrared images. The method has been evaluated on several databases with promising results on the UTFVP database as a main result. In including the model as a preprocessing in the biometric pipelines of recognition for finger vein patterns, the error rate has been reduced from 2.1% to 1.0%.
- KonferenzbeitragImproved Post-quantum-secure Face Template Protection System Based on Packed Homomorphic Encryption(BIOSIG 2021 - Proceedings of the 20th International Conference of the Biometrics Special Interest Group, 2021) Tamiya, Hiroto; Isshiki, Toshiyuki; Mori, Kengo; Obana, Satoshi; Ohki, TetsushiThis paper proposes an efficient face template protection system based on homomorphic encryption. By developing a message packing method suitable for the calculation of the square Euclidean distance, the proposed system computes the squared Euclidean distance between facial features by a single homomorphic multiplication. Our experimental results show the transaction time of the proposed system is about 14 times faster than that of the existing face template protection system based on homomorphic encryption presented in BIOSIG2020.
- KonferenzbeitragQualFace: Adapting Deep Learning Face Recognition for ID and Travel Documents with Quality Assessment(BIOSIG 2021 - Proceedings of the 20th International Conference of the Biometrics Special Interest Group, 2021) Tremoço, João; Medvedev, Iurii; Gonçalves, NunoModern face recognition biometrics widely rely on deep neural networks that are usually trained on large collections of wild face images of celebrities. This choice of the data is related with its public availability in a situation when existing ID document compliant face image datasets (usually stored by national institutions) are hardly accessible due to continuously increasing privacy restrictions. However this may lead to a leak in performance in systems developed specifically for ID document compliant images. In this work we proposed a novel face recognition approach for mitigating that problem. To adapt deep face recognition network for document security purposes, we propose to regularise the training process with specific sample mining strategy which penalises the samples by their estimated quality, where the quality metric is proposed by our work and is related to the specific case of face images for ID documents. We perform extensive experiments and demonstrate the efficiency of proposed approach for ID document compliant face images.
- KonferenzbeitragMy Eyes Are Up Here: Promoting Focus on Uncovered Regions in Masked Face Recognition(BIOSIG 2021 - Proceedings of the 20th International Conference of the Biometrics Special Interest Group, 2021) Neto, Pedro; Boutros, Fadi; Pinto, João Ribeiro; Saffari, Mohsen; Damer, Naser; Sequeira, Ana F.; Cardoso, Jaime S.The recent Covid-19 pandemic and the fact that wearing masks in public is now mandatory in several countries, created challenges in the use of face recognition systems (FRS). In this work, we address the challenge of masked face recognition (MFR) and focus on evaluating the verification performance in FRS when verifying masked vs unmasked faces compared to verifying only unmasked faces. We propose a methodology that combines the traditional triplet loss and the mean squared error (MSE) intending to improve the robustness of an MFR system in the masked-unmasked comparison mode. The results obtained by our proposed method show improvements in a detailed step-wise ablation study. The conducted study showed significant performance gains induced by our proposed training paradigm and modified triplet loss on two evaluation databases.
- KonferenzbeitragIdentical Twins as a Facial Similarity Benchmark for Human Facial Recognition(BIOSIG 2021 - Proceedings of the 20th International Conference of the Biometrics Special Interest Group, 2021) McCauley, John; Soleymani, Sobhan; Williams, Brady; Nasrabadi, Nasser; Dawson, JeremyThe problem of distinguishing identical twins and non-twin look-alikes in automated facial recognition (FR) applications has become increasingly important with the widespread adoption of facial biometrics. This work presents an application of one of the largest twin datasets compiled to date to address two FR challenges: 1) determining a baseline measure of facial similarity between identical twins and 2) applying this similarity measure to determine the impact of doppelgangers, or look-alikes, on FR performance for large face datasets. The facial similarity measure is determined via a deep Siamese convolutional neural network. The proposed network provides a quantitative similarity score for any two given faces and has been applied to large-scale face datasets to identify similar face pairs.
- KonferenzbeitragLearning by Environment Cluster s for Face Presentation Attack Detection(BIOSIG 2021 - Proceedings of the 20th International Conference of the Biometrics Special Interest Group, 2021) Matsunami, Tomoaki; Uchida, Hidetsugu; Abe, Narishige; Yamada, ShigefumiFace recognition has been used widely for personal authentication. However, there is a problem that it is vulnerable to a presentation attack in which a counterfeit such as a photo is presented to a camera to impersonate another person. Although various presentation attack detection methods have been proposed, these methods have not been able to sufficiently cope with the diversity of the heterogeneous environments including presentation attack instruments (PAIs) and lighting conditions. In this paper, we propose Learning by Environment Clusters (LEC) which divides training data into some clusters of similar photographic environments and trains bona-fide and attack classification models for each cluster. Experimental results using Replay-Attack, OULU-NPU, and CelebA-Spoof show the EER of the conventional method which trains one classification model from all data was 20.0%, but LEC can achieve 13.8% EER when using binarized statistical image features (BSIFs) and support vector machine used as the classification method