22 research outputs found
The Multiscenario Multienvironment BioSecure Multimodal Database (BMDB)
A new multimodal biometric database designed and acquired within the framework of the European BioSecure Network of Excellence is presented. It is comprised of more than 600 individuals acquired simultaneously in three scenarios: 1 over the Internet, 2 in an office environment with desktop PC, and 3 in indoor/outdoor environments with mobile portable hardware. The three scenarios include a common part of audio/video data. Also, signature and fingerprint data have been acquired both with desktop PC and mobile portable hardware. Additionally, hand and iris data were acquired in the second scenario using desktop PC. Acquisition has been conducted by 11 European institutions. Additional features of the BioSecure Multimodal Database (BMDB) are: two acquisition sessions, several sensors in certain modalities, balanced gender and age distributions, multimodal realistic scenarios with simple and quick tasks per modality, cross-European diversity, availability of demographic data, and compatibility with other multimodal databases. The novel acquisition conditions of the BMDB allow us to perform new challenging research and evaluation of either monomodal or multimodal biometric systems, as in the recent BioSecure Multimodal Evaluation campaign. A description of this campaign including baseline results of individual modalities from the new database is also given. The database is expected to be available for research purposes through the BioSecure Association during 2008
The Multiscenario Multienvironment BioSecure Multimodal Database (BMDB)
A new multimodal biometric database designed and acquired within the
framework of the European BioSecure Network of Excellence is presented. It is
comprised of more than 600 individuals acquired simultaneously in three
scenarios: 1) over the Internet, 2) in an office environment with desktop PC,
and 3) in indoor/outdoor environments with mobile portable hardware. The three
scenarios include a common part of audio/video data. Also, signature and
fingerprint data have been acquired both with desktop PC and mobile portable
hardware. Additionally, hand and iris data were acquired in the second scenario
using desktop PC. Acquisition has been conducted by 11 European institutions.
Additional features of the BioSecure Multimodal Database (BMDB) are: two
acquisition sessions, several sensors in certain modalities, balanced gender
and age distributions, multimodal realistic scenarios with simple and quick
tasks per modality, cross-European diversity, availability of demographic data,
and compatibility with other multimodal databases. The novel acquisition
conditions of the BMDB allow us to perform new challenging research and
evaluation of either monomodal or multimodal biometric systems, as in the
recent BioSecure Multimodal Evaluation campaign. A description of this campaign
including baseline results of individual modalities from the new database is
also given. The database is expected to be available for research purposes
through the BioSecure Association during 2008Comment: Published at IEEE Transactions on Pattern Analysis and Machine
Intelligence journa
A review on Person Authentication using Finger Vein Technique
Biometric system has been actively emerging in various industries and continuing to roll to provide higher security features for access control system. The proposed system simultaneously acquires the finger surface and subsurface features from finger-vein and finger print images. This paper reviews the acquired finger vein and finger texture images are first subjected to pre-processing steps, which extract the region-of-interest (ROI). The enhanced and normalized ROI images employed to extract features and generate matching score. For this I will develop and investigate two new score-level combinations i.e. Gabour filter, Repeated line Tracking and Neural network comparatively evaluate them more popular score-level fusion approaches to ascertain their effectiveness in the proposed system. MATLAB software will be using for proposed work
Preprocessing and feature selection for improved sensor interoperability in online biometric signature verification
Under a IEEE Open Access Publishing Agreement.Due to the technological evolution and the increasing popularity of smartphones, people can access an application using authentication based on biometric approaches from many different devices. Device interoperability is a very challenging problem for biometrics, which needs to be further studied. In this paper, we focus on interoperability device compensation for online signature verification since this biometric trait is gaining a significant interest in banking and commercial sector in the last years. The proposed approach is based on two main stages. The first one is a preprocessing stage where data acquired from different devices are processed in order to normalize the signals in similar ranges. The second one is based on feature selection taking into account the device interoperability case, in order to select to select features which are robust in these conditions. This proposed approach has been successfully applied in a similar way to two common system approaches in online signature verification, i.e., a global features-based system and a time functions-based system. Experiments are carried out using Biosecure DS2 (Wacom device) and DS3 (Personal Digital Assistant mobile device) dynamic signature data sets which take into account multisession and two different scenarios emulating real operation conditions. The performance of the proposed global features-based and time functions-based systems applying the two main stages considered in this paper have provided an average relative improvement of performance of 60.3% and 26.5% Equal Error Rate (EER), respectively, for random forgeries cases, compared with baseline systems. Finally, a fusion of the proposed systems has achieved a further significant improvement for the device interoperability problem, especially for skilled forgeries. In this case, the proposed fusion system has achieved an average relative improvement of 27.7% EER compared with the best performance of time functions-based system. These results prove the robustness of the proposed approach and open the door for future works using devices as smartphones or tablets, commonly used nowadays.This work was supported in part by the Project Bio-Shield under Grant TEC2012-34881, in part by Cecabank e-BioFirma Contract, and in part by Catedra UAM-Telefonic
Morphing Attack Detection -- Database, Evaluation Platform and Benchmarking
Morphing attacks have posed a severe threat to Face Recognition System (FRS).
Despite the number of advancements reported in recent works, we note serious
open issues such as independent benchmarking, generalizability challenges and
considerations to age, gender, ethnicity that are inadequately addressed.
Morphing Attack Detection (MAD) algorithms often are prone to generalization
challenges as they are database dependent. The existing databases, mostly of
semi-public nature, lack in diversity in terms of ethnicity, various morphing
process and post-processing pipelines. Further, they do not reflect a realistic
operational scenario for Automated Border Control (ABC) and do not provide a
basis to test MAD on unseen data, in order to benchmark the robustness of
algorithms. In this work, we present a new sequestered dataset for facilitating
the advancements of MAD where the algorithms can be tested on unseen data in an
effort to better generalize. The newly constructed dataset consists of facial
images from 150 subjects from various ethnicities, age-groups and both genders.
In order to challenge the existing MAD algorithms, the morphed images are with
careful subject pre-selection created from the contributing images, and further
post-processed to remove morphing artifacts. The images are also printed and
scanned to remove all digital cues and to simulate a realistic challenge for
MAD algorithms. Further, we present a new online evaluation platform to test
algorithms on sequestered data. With the platform we can benchmark the morph
detection performance and study the generalization ability. This work also
presents a detailed analysis on various subsets of sequestered data and
outlines open challenges for future directions in MAD research.Comment: This paper is a pre-print. The article is accepted for publication in
IEEE Transactions on Information Forensics and Security (TIFS