Trusted Biometrics under Spoofing Attacks

Identity management for persons using biometrics has become a reality mainly because of the biometric passport but also because of the presence of more and more biometricenabled applications for personal computers. Although, the market for identity management using biometrics is dominated by some key players focusing mainly on high security applications, new exploitation routes are currently explored by SMEs. This market of identity management using biometrics is thus growing rapidly. Unfortunately, it has been shown recently that conventional biometric techniques, such as fingerprint or face recognition, are vulnerable to attacks. Generally, two types of attacks are considered: (1) indirect attacks due to intruders, such as cyber-criminal hackers, and (2) direct (spoofing) attacks, where a person tries masquerade as another one by falsifying data and thereby gaining an illegitimate advantage. Currently, spoofing attacks are a major problem for companies willing to market identity management solutions based on biometric technologies. Thus there is need for efficient and reliable solutions for detecting and circumventing these direct attacks. The typical countermeasure to a spoofing attack liveness detection that aims at detecting physiological signs of life. Another possible countermeasure is multi-modal biometrics. Indeed, voice recognition for instance could be performed jointly to face recognition and would be more robust to an attack on the video stream. Similarly, gait, face and iris recognition could be performed jointly. Additionally, it has been shown recently in pioneering work that emerging biometrics such as gait, vein or electro-physiological signals are potentially very difficult or impossible to spoof. The project should investigate as well such novel biometrics and further explore their advantages and limits. Hence, the goal of this proposal is to research, develop, evaluate and transfer antispoofing solutions.
Application Area - Security and risk management, Biometric Person Recognition
Idiap Research Institute AG, INSTITUTE OF AUTOMATION CHINESE ACADEMY OF SCIENCES, CENTRE FOR SCIENCE, SOCIETY AND CITIZENSHIP, EURECOM, KeyLemon SA ams, SAGEM SECURITE S.A., STARLAB BARCELONA SL, Universidad Autonoma de Madrid, UNIVERSITA DEGLI STUDI DI CAGLIARI, University of Oulu, Center for Machine Vision and Signal Analysis, University of Southampton
Seventh Framework Programme
Nov 01, 2010
Jun 30, 2014