BIOSIG 2019

Lena Klasen - Keynote Speaker

Swedish Forensic Laboratory

Operational Biometric Systems and the Need for Future Research

Abstract

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.

CV

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

Abstract

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.

CV

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

Abstract

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.

CV

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.