8,776 research outputs found
Biometric system verification close to "real world" conditions
The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-642-04391-8_31Proceedings of Joint COST 2101 and 2102 International Conference, BioID_MultiComm 2009, Madrid, Spain.In this paper we present an autonomous biometric device developed in the framework of a national project. This system is able to capture speech, hand-geometry, online signature and face, and can open a door when the user is positively verified. Nevertheless the main purpose is to acquire a database without supervision (normal databases are collected in the presence of a supervisor that tells you what to do in front of the device, which is an unrealistic situation). This system will permit us to explain the main differences between what we call "real conditions" as opposed to "laboratory conditions".This work has been supported by FEDER and MEC, TEC2006-13141-C03/TCM, and COST-2102
FaceQnet: Quality Assessment for Face Recognition based on Deep Learning
In this paper we develop a Quality Assessment approach for face recognition
based on deep learning. The method consists of a Convolutional Neural Network,
FaceQnet, that is used to predict the suitability of a specific input image for
face recognition purposes. The training of FaceQnet is done using the VGGFace2
database. We employ the BioLab-ICAO framework for labeling the VGGFace2 images
with quality information related to their ICAO compliance level. The
groundtruth quality labels are obtained using FaceNet to generate comparison
scores. We employ the groundtruth data to fine-tune a ResNet-based CNN, making
it capable of returning a numerical quality measure for each input image.
Finally, we verify if the FaceQnet scores are suitable to predict the expected
performance when employing a specific image for face recognition with a COTS
face recognition system. Several conclusions can be drawn from this work, most
notably: 1) we managed to employ an existing ICAO compliance framework and a
pretrained CNN to automatically label data with quality information, 2) we
trained FaceQnet for quality estimation by fine-tuning a pre-trained face
recognition network (ResNet-50), and 3) we have shown that the predictions from
FaceQnet are highly correlated with the face recognition accuracy of a
state-of-the-art commercial system not used during development. FaceQnet is
publicly available in GitHub.Comment: Preprint version of a paper accepted at ICB 201
THRIVE: Threshold Homomorphic encryption based secure and privacy preserving bIometric VErification system
In this paper, we propose a new biometric verification and template
protection system which we call the THRIVE system. The system includes novel
enrollment and authentication protocols based on threshold homomorphic
cryptosystem where the private key is shared between a user and the verifier.
In the THRIVE system, only encrypted binary biometric templates are stored in
the database and verification is performed via homomorphically randomized
templates, thus, original templates are never revealed during the
authentication stage. The THRIVE system is designed for the malicious model
where the cheating party may arbitrarily deviate from the protocol
specification. Since threshold homomorphic encryption scheme is used, a
malicious database owner cannot perform decryption on encrypted templates of
the users in the database. Therefore, security of the THRIVE system is enhanced
using a two-factor authentication scheme involving the user's private key and
the biometric data. We prove security and privacy preservation capability of
the proposed system in the simulation-based model with no assumption. The
proposed system is suitable for applications where the user does not want to
reveal her biometrics to the verifier in plain form but she needs to proof her
physical presence by using biometrics. The system can be used with any
biometric modality and biometric feature extraction scheme whose output
templates can be binarized. The overall connection time for the proposed THRIVE
system is estimated to be 336 ms on average for 256-bit biohash vectors on a
desktop PC running with quad-core 3.2 GHz CPUs at 10 Mbit/s up/down link
connection speed. Consequently, the proposed system can be efficiently used in
real life applications
MobiBits: Multimodal Mobile Biometric Database
This paper presents a novel database comprising representations of five
different biometric characteristics, collected in a mobile, unconstrained or
semi-constrained setting with three different mobile devices, including
characteristics previously unavailable in existing datasets, namely hand
images, thermal hand images, and thermal face images, all acquired with a
mobile, off-the-shelf device. In addition to this collection of data we perform
an extensive set of experiments providing insight on benchmark recognition
performance that can be achieved with these data, carried out with existing
commercial and academic biometric solutions. This is the first known to us
mobile biometric database introducing samples of biometric traits such as
thermal hand images and thermal face images. We hope that this contribution
will make a valuable addition to the already existing databases and enable new
experiments and studies in the field of mobile authentication. The MobiBits
database is made publicly available to the research community at no cost for
non-commercial purposes.Comment: Submitted for the BIOSIG2018 conference on June 18, 2018. Accepted
for publication on July 20, 201
Relations among Security Metrics for Template Protection Algorithms
Many biometric template protection algorithms have been proposed mainly in
two approaches: biometric feature transformation and biometric cryptosystem.
Security evaluation of the proposed algorithms are often conducted in various
inconsistent manner. Thus, it is strongly demanded to establish the common
evaluation metrics for easier comparison among many algorithms. Simoens et al.
and Nagar et al. proposed good metrics covering nearly all aspect of
requirements expected for biometric template protection algorithms. One
drawback of the two papers is that they are biased to experimental evaluation
of security of biometric template protection algorithms. Therefore, it was
still difficult mainly for algorithms in biometric cryptosystem to prove their
security according to the proposed metrics. This paper will give a formal
definitions for security metrics proposed by Simoens et al. and Nagar et al. so
that it can be used for the evaluation of both of the two approaches. Further,
this paper will discuss the relations among several notions of security
metrics
In-ear EEG biometrics for feasible and readily collectable real-world person authentication
The use of EEG as a biometrics modality has been investigated for about a
decade, however its feasibility in real-world applications is not yet
conclusively established, mainly due to the issues with collectability and
reproducibility. To this end, we propose a readily deployable EEG biometrics
system based on a `one-fits-all' viscoelastic generic in-ear EEG sensor
(collectability), which does not require skilled assistance or cumbersome
preparation. Unlike most existing studies, we consider data recorded over
multiple recording days and for multiple subjects (reproducibility) while, for
rigour, the training and test segments are not taken from the same recording
days. A robust approach is considered based on the resting state with eyes
closed paradigm, the use of both parametric (autoregressive model) and
non-parametric (spectral) features, and supported by simple and fast cosine
distance, linear discriminant analysis and support vector machine classifiers.
Both the verification and identification forensics scenarios are considered and
the achieved results are on par with the studies based on impractical on-scalp
recordings. Comprehensive analysis over a number of subjects, setups, and
analysis features demonstrates the feasibility of the proposed ear-EEG
biometrics, and its potential in resolving the critical collectability,
robustness, and reproducibility issues associated with current EEG biometrics
Biometric Authentication System on Mobile Personal Devices
We propose a secure, robust, and low-cost biometric authentication system on the mobile personal device for the personal network. The system consists of the following five key modules: 1) face detection; 2) face registration; 3) illumination normalization; 4) face verification; and 5) information fusion. For the complicated face authentication task on the devices with limited resources, the emphasis is largely on the reliability and applicability of the system. Both theoretical and practical considerations are taken. The final system is able to achieve an equal error rate of 2% under challenging testing protocols. The low hardware and software cost makes the system well adaptable to a large range of security applications
Video-based driver identification using local appearance face recognition
In this paper, we present a person identification system for vehicular environments. The proposed system uses face images of the driver and utilizes local appearance-based face recognition over the video sequence. To perform local appearance-based face recognition, the input face image is decomposed into non-overlapping blocks and on each local block discrete cosine transform is applied to extract the local features. The extracted local features are then combined to construct the overall feature vector. This process is repeated for each video frame. The distribution of the feature vectors over the video are modelled using a Gaussian distribution function at the training stage. During testing, the feature vector extracted from each frame is compared to each personâs distribution, and individual likelihood scores are generated. Finally, the person is identified as the one who has maximum joint-likelihood score. To assess the performance of the developed system, extensive experiments are conducted on different identification scenarios, such as closed set identification, open set identification and verification. For the experiments a subset of the CIAIR-HCC database, an in-vehicle data corpus that is collected at the Nagoya University, Japan is used. We show that, despite varying environment and illumination conditions, that commonly exist in vehicular environments, it is possible to identify individuals robustly from their face images. Index Terms â Local appearance face recognition, vehicle environment, discrete cosine transform, fusion. 1
Face recognition technologies for evidential evaluation of video traces
Human recognition from video traces is an important task in forensic investigations and evidence evaluations. Compared with other biometric traits, face is one of the most popularly used modalities for human recognition due to the fact that its collection is non-intrusive and requires less cooperation from the subjects. Moreover, face images taken at a long distance can still provide reasonable resolution, while most biometric modalities, such as iris and fingerprint, do not have this merit. In this chapter, we discuss automatic face recognition technologies for evidential evaluations of video traces. We first introduce the general concepts in both forensic and automatic face recognition , then analyse the difficulties in face recognition from videos . We summarise and categorise the approaches for handling different uncontrollable factors in difficult recognition conditions. Finally we discuss some challenges and trends in face recognition research in both forensics and biometrics . Given its merits tested in many deployed systems and great potential in other emerging applications, considerable research and development efforts are expected to be devoted in face recognition in the near future
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