18th International Conference of the Biometrics Special Interest Group
Biometrics provides efficient and reliable solutions to recognize individuals. With increasing number of identity theft and miss-use incidents we do observe a significant fraud in e-commerce and thus growing interests on trustworthiness of person authentication.
Nowadays we find biometric applications in areas like border control, national ID cards, e-banking, e-commerce, e-health etc. Large-scale applications such as the European Union SmartBorder Concept, the Visa Information System (VIS) and Unique Identification (UID) in India require high accuracy and also reliability, interoperability, scalability and usability. Many of these are joint requirements also for forensic applications.
Multimodal biometrics combined with fusion techniques can improve recognition performance. Efficient searching or indexing methods can accelerate identification efficiency. Additionally, quality of captured biometric samples can strongly influence the performance. Moreover, mobile biometrics is an emerging area and biometrics based smartphones can support deployment and acceptance of biometric systems.
However, concerns about security and privacy cannot be neglected. The relevant techniques in the area of presentation attack detection (liveness detection) and template protection are about to supplement biometric systems, in order to improve fake resistance, prevent potential attacks such as cross matching, identity theft etc. The 18th International Conference of the Biometrics Special Interest Group (BIOSIG 2019) conference addresses these issues and will present innovations and best practices that can be transferred into future applications.
Antitza Dantcheva, Christian Rathgeb, Andreas Uhl
Victor Philipp Busch
Alexander Nouak, Claudia Prediger
Wednesday, September 18
EAB Award ceremony and BIOSIG welcome reception
BIOSIG - MAIN CONFERENCE - Room 074
Thursday, September 19
Thursday, September 19
Christoph Busch (Hochschule Darmstadt)
BIOSIG Conference Opening
LENA KLASEN (Swedish Forensic Laboratory)
KEYNOTE: Operational Biometric Systems and the Need for Future Research
Christof Kauba (University of Salzburg)
Public Perceptions and Preferences towards ATMs with Biometric Authentication in Austria
Annalisa Franco (University of Bologna)
Decoupling texture blending and shape warping in face morphing
Clemens Seibold (Fraunhofer HHI)
Style Your Face Morph and Improve Your Face Morphing Attack Detector
Ana F. Sequeira (INESC TEC)
Adversarial learning for a robust iris presentation attack detection method against unseen attack presentations
Ehsaneddin Jalilian (University of Salzburg)
Deep Domain Adaption for Convolutional Neural Network (CNN) based Iris Segmentation: Solutions and Pitfalls
Martin Drahansky (Brno University of Technology)
Is There Any Similarity Between a Person’s Left and Right Retina?
Arslan Brömme & Christian Rathgeb
Benjamin Dieckmann (secunet)
Fingerprint Pre-Alignment based on Deep Learning
Arslan Brömme & Christian Rathgeb
Yoshinori Koda (NEC Corporation)
Development of 2,400ppi Fingerprint Sensor for Capturing Neonate Fingerprint within 24 Hours after Birth
Opening Poster Session
University of Buckingham
Android Pattern Unlock Authentication - effectiveness of local and global dynamic features
IZTECH Computer Engineering Department
Impact of variations in synthetic training data on fingerprint classification
University of Southampton
Gender and Kinship by Model-Based Ear Biometrics
Brno University of Technology
Psoriasis Damage Simulation into Synthetic Fingerprint
On the Application of Homomorphic Encryption to Face Identification
Anomalies in measuring speed and other dynamic properties with touchscreens and tablets or “Mr. Policeman, are you sure my car was going that fast?”
University of Beira Interior
Region-Based CNNs for Pedestrian Gender Recognition in Visual Surveillance Environments
Iyyakutti Iyappan Ganapathi
Multi-resolution Local Descriptor for 3D Ear Recognition
Istanbul Technical University
Thermal to Visible Face Recognition Using Deep Autoencoders
Social Event: Dinner with Barbeque
BIOSIG - MAIN CONFERENCE - Room 074
Friday, September 20
Friday, September 20
Takashi Shinzaki (Fujitsu Research Laboratory)
KEYNOTE: Evolution and Use Cases of Palm Vein Authentication
Bernhard Prommegger (University of Salzburg)
Perspective Multiplication for Multi-Perspective Enrolment in Finger Vein Recognition
Jascha Kolberg (Hochschule Darmstadt)
Multi-algorithm Benchmark for Fingerprint Presentation Attack Detection with Laser Speckle Contrast Imaging
Blaine Ayotte (Clarkson University)
Fast and Accurate Continuous User Authentication by Fusion of Instance-based, Free-text Keystroke Dynamics
Vijaya Krishna Yalavarthi (University of Hildesheim)
Gait verification using deep learning with a pairwise loss
Pendar Alirezazadeh (University of Beira Interior)
Pose Switch-based Convolutional Neural Network for Clothing Analysis in Visual Surveillance Environment
Walter Scheirer (University of Notre Dame)
KEYNOTE: The Limits and Potentials of Deep Learning for Facial Analysis
Christoph Busch (Hochschule Darmstadt)
Lena Klasen - Keynote Speaker
Swedish Forensic Laboratory
Operational Biometric Systems and the Need for Future Research
The Swedish Police Authority is in the process of expanding applied research, coupled to the societal challenges and the possibilities brought from basic research results, to provide new tools and methods to improve capacity and support the daily operations. Building applied research at the Swedish Police Authority implies several challenges when striving for better means to prevent and handle crimes. In the end, the goal is the same - to find and bring perpetrators to justice, and thus biometric identification is a key issue. Locard's exchange principle, formulated as "Every contact leaves a trace" states that a criminal who brings traces to a crime scene, or leave the scene with traces from the scene, can be identified through these traces, such as DNA or fingerprints. Locard’s principle is still valid even if we nowadays spread numerous digital traces through all kinds of actions and leave traces in all kinds of digital media, bringing the needs for new methods that contribute to new and better way to investigate crimes, and consequently there is a need for improvements and expansion of the biometric concepts. For example AI/machine learning now contributes to another “revolution” also in the criminal justice system and examples from the process of building applied research within the field of person identification will be presented. This includes ongoing projects and biometric methods in use at the Swedish Police Authority. Moreover, the number and types of cameras rapidly increases. Using the right cameras for the problem at hand can improve the operational capacity in crime investigations. An ongoing project within the Swedish Visual Sweden initiative, on intelligent n-dimensional modelling by multidimensional sensor informatics for computer vision and visualization will be presented. The concept aims to bring 3D modelling to a new level by adding more dimensions when imaging a scene. Sensor information from n-dimensional sensor systems is thus a valuable source of data to conduct research for new means for biometric identification. For example, tracking can be supported from facial identification, thermal signature can be used to indicate human motion, color and spectral signatures from clothing can support tracking and range data to support estimation of shapes and motion trajectories.
Lena Klasén has a PhD in Image Coding at Linköping University, Sweden and has led industrial- and research organizations such as Swedish Police Authority, Saab AB, Swedish Defence research Agency, Swedish Defence Material Administration, Swedish National Laboratory of Forensic Science and the Implementing Committee of the New Swedish Police organization at the Swedish Ministry of Justice. Dr. Klasén is since 2018 Research Director at the Swedish Police Authority, with the task to build up, establish and coordinate applied research to provide new tools and methods for the Swedish Police force. January 2015 to November 2018 she was appointed Director of the Swedish National Forensic Center (formerly Swedish National Laboratory of Forensic Science). Lena has held several commissions of trust, e g as board member and expert, including being appointed as forensic expert by the US Department of Justice. Her industrial experience includes product portfolio management at Saab and being involved in starting up innovative companies.
Takashi Shinzaki - Keynote Speaker
Fujitsu Research Laboratory, JP
Evolution and Use Cases of Palm Vein Authentication
Palm vein authentication is a vein feature authentication technology that uses palm veins as the biometric feature. Palm vein patterns are normally captured using near-infrared light via either the reflection or the transmission methods. Because veins are under the skin of humans and are not affected by the skin surface condition, a vein authentication shows stable authentication performance. Moreover, because palm vein patterns are diverse and complex, they give sufficient information to identify one individual among a large population. As a contactless type of biometric authentication, it is suitable for use in applications that require a high level of hygiene or for use in public applications. Several banks have been using palm vein authentication for ATM security since July 2004. Our palm vein authentication technology has expanded its application field by downsizing the sensor and improving the authentication performance. The palm vein authentication has been used in a variety of applications such as door security systems, login management systems for PCs, financial services, payment services, patient identification systems in hospitals and airport security systems. This presentation introduces the technical outline of palm vein authentication and its use cases.
Takashi Shinzaki is the principal researcher at Fujitsu Laboratories Ltd. and is responsible for making authentication solution strategies. He has more than 31 years’ experiences in various kinds of projects related biometric authentication. His main expertise in authentication methods are fingerprint authentication and vein authentication. He led a project to develop an appliance-type biometric authentication server for a pc login application in 2002. He contributed to the introduction of fingerprint authentication on mass-produced cell phones launched in Japan in 2003. He contributed to realize a laptop PC with palm vein authentication in 2011. He has been aiming to deploy biometric authentication technologies in general social life. And he is interested in the research and development of all the technologies necessary to realize it. He has received the 67th Electrical Science and Engineering Promotion Awards in 2017 for his development of mobile fingerprint authentication technology. He serves as the Japanese head of delegation of ISO/IEC JTC1 SC37.
Walter Scheirer - Keynote Speaker
University of Notre Dame, U.S.
The Limits and Potentials of Deep Learning for Facial Analysis
Deep learning-based approaches for tasks in facial analysis yield remarkable performance, but do they have any practical limits? Moreover, with the flexibility afforded by artificial neural networks, what new potentials does such technology open up? In the first part of this talk, the limits of convolutional neural networks (CNNs) for face recognition will be assessed via a new evaluation regime grounded in psychological experimentation. Scientific fields that are interested in faces have developed their own sets of concepts and procedures for understanding how a target model system (be it a person or algorithm) perceives a face under varying conditions. In computer vision, this has largely been in the form of dataset evaluation for recognition tasks where summary statistics are used to measure progress. While aggregate performance has continued to improve, understanding individual causes of failure has been difficult, as it is not always clear why a particular face fails to be recognized, or why an impostor is recognized by an algorithm. Importantly, other fields studying vision have addressed this via the use of visual psychophysics: the controlled manipulation of stimuli and careful study of the responses they evoke in a model system. In this talk, I will suggest that visual psychophysics is a viable methodology for making face recognition algorithms more explainable. A comprehensive set of procedures is developed for assessing face recognition algorithm behavior, which is then deployed over state-of-the-art CNNs and more basic, yet still widely used, shallow and handcrafted feature-based approaches.
In the second part of this talk, the potential of CNNs for describable visual facial attribute modeling will be explored. Attributes are now commonplace in human biometrics and affective computing, with existing algorithms even reaching a sufficient point of maturity for placement into commercial products. These algorithms model objective facets of facial appearance, such as hair and eye color, expression, and aspects of the geometry of the face. A natural extension, which has not been studied to any great extent thus far, is the ability to model subjective attributes that are assigned to a face based purely on visual judgments. For instance, with just a glance, our first impression of a face may lead us to believe that a person is smart, worthy of our trust, and perhaps even our admiration — regardless of the underlying truth behind such attributes. Psychologists believe that these judgments are based on a variety of factors such as emotional states, personality traits, and other physiognomic cues. But work in this direction leads to an interesting question: how do we create models for problems where there is only measurable behavior? Here I will introduce a regression framework that allows us to train predictive models of crowd behavior for social attribute assignment. Over images from the AFLW face database, these models demonstrate strong correlations with human crowd ratings.
Walter J. Scheirer, Ph.D. is an Assistant Professor in the Department of Computer Science and Engineering at the University of Notre Dame. Previously, he was a postdoctoral fellow at Harvard University, with affiliations in the School of Engineering and Applied Sciences, Dept. of Molecular and Cellular Biology and Center for Brain Science, and the director of research & development at Securics, Inc., an early stage company producing innovative biometrics solutions. He received his Ph.D. from the University of Colorado and his M.S. and B.A. degrees from Lehigh University.
Dr. Scheirer has extensive experience in the areas of human biometrics, computer vision, machine learning and artificial intelligence. His overarching research interest is the fundamental problem of recognition, including the representations and algorithms supporting solutions to it. He has made important contributions to the field of biometrics through his work on open set recognition, extreme value theory statistics for visual recognition, and template protection. His recent work has explored the intersection between neuroscience and computer science, leading to new, biologically-informed, ways to evaluate and improve algorithms. .
He is very active within the biometrics and computer vision communities, having served as the program chair of IEEE/IAPR IJCB, IEEE WACV, and the SPIE Conference on Biometric and Surveillance Technology for Human and Activity Identification. Dr. Scheirer is also a regular organizer of IEEE/CVF CVPR, and sits on the board of the Computer Vision Foundation. From 2016-2019, he was an editorial board member of the IEEE Biometrics Compendium.