KEYWORDS: Personal digital assistants, Biometrics, Data modeling, Databases, Oxygen, Light sources and illumination, Data fusion, Performance modeling, Information security, Mobile devices
Verification of a person's identity by the combination of more than one biometric trait strongly increases the robustness of person authentication in real applications. This is particularly the case in applications involving signals of degraded quality, as for person authentication on mobile platforms. The context of mobility generates degradations of input signals due to the variety of environments encountered (ambient noise, lighting variations, etc.), while the sensors' lower quality further contributes to decrease in system performance. Our aim in this work is to combine traits from the three biometric modalities of speech, face and handwritten signature in a concrete application, performing non intrusive biometric verification on a personal mobile device (smartphone/PDA).
Most available biometric databases have been acquired in more or less controlled environments, which makes it difficult to predict performance in a real application. Our experiments are performed on a database acquired on a PDA as part of the SecurePhone project (IST-2002-506883 project "Secure Contracts Signed by Mobile Phone"). This database contains 60 virtual subjects balanced in gender and age. Virtual subjects are obtained by coupling audio-visual signals from real English speaking subjects with signatures from other subjects captured on the touch screen of the PDA. Video data for the PDA database was recorded in 2 recording sessions separated by at least one week. Each session comprises 4 acquisition conditions: 2 indoor and 2 outdoor recordings (with in each case, a good and a degraded quality recording). Handwritten signatures were captured in one session in realistic conditions. Different scenarios of matching between training and test conditions are tested to measure the resistance of various fusion systems to different types of variability and different amounts of enrolment data.
KEYWORDS: Databases, Biometrics, Data modeling, Personal digital assistants, Mobile devices, 3D modeling, Cell phones, Systems modeling, Data fusion, Prototyping
In this article we test a number of score fusion methods for the purpose of multimodal biometric authentication. These tests were made for the SecurePhone project, whose aim is to develop a prototype mobile communication system enabling biometrically authenticated users to deal legally binding m-contracts during a mobile phone call on a PDA. The three biometrics of voice, face and signature were selected because they are all traditional non-intrusive and easy to use means of authentication which can readily be captured on a PDA. By combining multiple biometrics of relatively low security it may be possible to obtain a combined level of security which is at least as high as that provided by a PIN or handwritten signature, traditionally used for user authentication. As the relative success of different fusion methods depends on the database used and tests made, the database we used was recorded on a suitable PDA (the Qtek2020) and the test protocol was designed to reflect the intended application scenario, which is expected to use short text prompts. Not all of the fusion methods tested are original. They were selected for their suitability for implementation within the constraints imposed by the application. All of the methods tested are based on fusion of the match scores output by each modality. Though computationally simple, the methods tested have shown very promising results. All of the 4 fusion methods tested obtain a significant performance increase.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.