411 research outputs found
An Extensive Review on Spectral Imaging in Biometric Systems: Challenges and Advancements
Spectral imaging has recently gained traction for face recognition in
biometric systems. We investigate the merits of spectral imaging for face
recognition and the current challenges that hamper the widespread deployment of
spectral sensors for face recognition. The reliability of conventional face
recognition systems operating in the visible range is compromised by
illumination changes, pose variations and spoof attacks. Recent works have
reaped the benefits of spectral imaging to counter these limitations in
surveillance activities (defence, airport security checks, etc.). However, the
implementation of this technology for biometrics, is still in its infancy due
to multiple reasons. We present an overview of the existing work in the domain
of spectral imaging for face recognition, different types of modalities and
their assessment, availability of public databases for sake of reproducible
research as well as evaluation of algorithms, and recent advancements in the
field, such as, the use of deep learning-based methods for recognizing faces
from spectral images
A Comparative Evaluation of Heart Rate Estimation Methods using Face Videos
This paper presents a comparative evaluation of methods for remote heart rate
estimation using face videos, i.e., given a video sequence of the face as
input, methods to process it to obtain a robust estimation of the subjects
heart rate at each moment. Four alternatives from the literature are tested,
three based in hand crafted approaches and one based on deep learning. The
methods are compared using RGB videos from the COHFACE database. Experiments
show that the learning-based method achieves much better accuracy than the hand
crafted ones. The low error rate achieved by the learning based model makes
possible its application in real scenarios, e.g. in medical or sports
environments.Comment: Accepted in "IEEE International Workshop on Medical Computing
(MediComp) 2020
Infrared face recognition: a comprehensive review of methodologies and databases
Automatic face recognition is an area with immense practical potential which
includes a wide range of commercial and law enforcement applications. Hence it
is unsurprising that it continues to be one of the most active research areas
of computer vision. Even after over three decades of intense research, the
state-of-the-art in face recognition continues to improve, benefitting from
advances in a range of different research fields such as image processing,
pattern recognition, computer graphics, and physiology. Systems based on
visible spectrum images, the most researched face recognition modality, have
reached a significant level of maturity with some practical success. However,
they continue to face challenges in the presence of illumination, pose and
expression changes, as well as facial disguises, all of which can significantly
decrease recognition accuracy. Amongst various approaches which have been
proposed in an attempt to overcome these limitations, the use of infrared (IR)
imaging has emerged as a particularly promising research direction. This paper
presents a comprehensive and timely review of the literature on this subject.
Our key contributions are: (i) a summary of the inherent properties of infrared
imaging which makes this modality promising in the context of face recognition,
(ii) a systematic review of the most influential approaches, with a focus on
emerging common trends as well as key differences between alternative
methodologies, (iii) a description of the main databases of infrared facial
images available to the researcher, and lastly (iv) a discussion of the most
promising avenues for future research.Comment: Pattern Recognition, 2014. arXiv admin note: substantial text overlap
with arXiv:1306.160
QUEST Hierarchy for Hyperspectral Face Recognition
Face recognition is an attractive biometric due to the ease in which photographs of the human face can be acquired and processed. The non-intrusive ability of many surveillance systems permits face recognition applications to be used in a myriad of environments. Despite decades of impressive research in this area, face recognition still struggles with variations in illumination, pose and expression not to mention the larger challenge of willful circumvention. The integration of supporting contextual information in a fusion hierarchy known as QUalia Exploitation of Sensor Technology (QUEST) is a novel approach for hyperspectral face recognition that results in performance advantages and a robustness not seen in leading face recognition methodologies. This research demonstrates a method for the exploitation of hyperspectral imagery and the intelligent processing of contextual layers of spatial, spectral, and temporal information. This approach illustrates the benefit of integrating spatial and spectral domains of imagery for the automatic extraction and integration of novel soft features (biometric). The establishment of the QUEST methodology for face recognition results in an engineering advantage in both performance and efficiency compared to leading and classical face recognition techniques. An interactive environment for the testing and expansion of this recognition framework is also provided
Multispectral Palmprint Encoding and Recognition
Palmprints are emerging as a new entity in multi-modal biometrics for human
identification and verification. Multispectral palmprint images captured in the
visible and infrared spectrum not only contain the wrinkles and ridge structure
of a palm, but also the underlying pattern of veins; making them a highly
discriminating biometric identifier. In this paper, we propose a feature
encoding scheme for robust and highly accurate representation and matching of
multispectral palmprints. To facilitate compact storage of the feature, we
design a binary hash table structure that allows for efficient matching in
large databases. Comprehensive experiments for both identification and
verification scenarios are performed on two public datasets -- one captured
with a contact-based sensor (PolyU dataset), and the other with a contact-free
sensor (CASIA dataset). Recognition results in various experimental setups show
that the proposed method consistently outperforms existing state-of-the-art
methods. Error rates achieved by our method (0.003% on PolyU and 0.2% on CASIA)
are the lowest reported in literature on both dataset and clearly indicate the
viability of palmprint as a reliable and promising biometric. All source codes
are publicly available.Comment: Preliminary version of this manuscript was published in ICCV 2011. Z.
Khan A. Mian and Y. Hu, "Contour Code: Robust and Efficient Multispectral
Palmprint Encoding for Human Recognition", International Conference on
Computer Vision, 2011. MATLAB Code available:
https://sites.google.com/site/zohaibnet/Home/code
Multispectral Imaging For Face Recognition Over Varying Illumination
This dissertation addresses the advantage of using multispectral narrow-band images over conventional broad-band images for improved face recognition under varying illumination. To verify the effectiveness of multispectral images for improving face recognition performance, three sequential procedures are taken into action: multispectral face image acquisition, image fusion for multispectral and spectral band selection to remove information redundancy.
Several efficient image fusion algorithms are proposed and conducted on spectral narrow-band face images in comparison to conventional images. Physics-based weighted fusion and illumination adjustment fusion make good use of spectral information in multispectral imaging process. The results demonstrate that fused narrow-band images outperform the conventional broad-band images under varying illuminations. In the case where multispectral images are acquired over severe changes in daylight, the fused images outperform conventional broad-band images by up to 78%. The success of fusing multispectral images lies in the fact that multispectral images can separate the illumination information from the reflectance of objects which is impossible for conventional broad-band images.
To reduce the information redundancy among multispectral images and simplify the imaging system, distance-based band selection is proposed where a quantitative evaluation metric is defined to evaluate and differentiate the performance of multispectral narrow-band images. This method is proved to be exceptionally robust to parameter changes.
Furthermore, complexity-guided distance-based band selection is proposed using model selection criterion for an automatic selection. The performance of selected bands outperforms the conventional images by up to 15%. From the significant performance improvement via distance-based band selection and complexity-guided distance-based band selection, we prove that specific facial information carried in certain narrow-band spectral images can enhance face recognition performance compared to broad-band images. In addition, both algorithms are proved to be independent to recognition engines.
Significant performance improvement is achieved by proposed image fusion and band selection algorithms under varying illumination including outdoor daylight conditions. Our proposed imaging system and image processing algorithms lead to a new avenue of automatic face recognition system towards a better recognition performance than the conventional peer system over varying illuminations
3D Face Reconstruction from Light Field Images: A Model-free Approach
Reconstructing 3D facial geometry from a single RGB image has recently
instigated wide research interest. However, it is still an ill-posed problem
and most methods rely on prior models hence undermining the accuracy of the
recovered 3D faces. In this paper, we exploit the Epipolar Plane Images (EPI)
obtained from light field cameras and learn CNN models that recover horizontal
and vertical 3D facial curves from the respective horizontal and vertical EPIs.
Our 3D face reconstruction network (FaceLFnet) comprises a densely connected
architecture to learn accurate 3D facial curves from low resolution EPIs. To
train the proposed FaceLFnets from scratch, we synthesize photo-realistic light
field images from 3D facial scans. The curve by curve 3D face estimation
approach allows the networks to learn from only 14K images of 80 identities,
which still comprises over 11 Million EPIs/curves. The estimated facial curves
are merged into a single pointcloud to which a surface is fitted to get the
final 3D face. Our method is model-free, requires only a few training samples
to learn FaceLFnet and can reconstruct 3D faces with high accuracy from single
light field images under varying poses, expressions and lighting conditions.
Comparison on the BU-3DFE and BU-4DFE datasets show that our method reduces
reconstruction errors by over 20% compared to recent state of the art
Face Recognition via Ensemble Sift Matching of Uncorrelated Hyperspectral Bands and Spectral PCTS
Face recognition is not a new area of study, but facial recognition using through hyperspectral images is a somewhat new concept which is still in its infancy. Although the conventional method of face recognition using Red-Green-Blue (RGB) or grayscale images has been advanced over the last twenty years, these methods are still shown to have weak performance whenever there are variations or changes in lighting, pose, or temporal aspect of the subjects. A hyperspectral representation of an image captures more information that is available within a scene than a RGB image therefore it is beneficial to study the performance of face recognition using a hyperspectral representation of the subjects\u27 faces. We studied the results of a variety of methods for performing face recognition using the Scale Invariant Transformation Feature (SIFT) algorithm as a matching function on uncorrelated spectral bands, principal component representation of the spectral bands, and the ensemble decision of the two. We conclude that there is no dominating method in the scope of our research; however, we do obtain three methods with leading performances despite some trade-off between performance at lower ranks and performance at higher ranks...that outperform the results obtained from a previous study which only considered a SIFT application on a single hyperspectral band which also performs very well under temporal variation
Review of Facial Recognition and Liveness Detect
Facial recognition technology has been dramatically integrated into almost all the aspects of human life, such as mobile payment, identification applications, security management, and criminal cases, etc. However, these applications can be easily fooled by deliberate spoofing strategies. To ensure the identifications of users and avoid being spoofed are the central cores of this technology. As a result, its safeness and accuracy issues attract researchers to dig into this field. In terms of present existing deception and spoofing strategies, liveness detection plays a significant role in improving the robustness of facial recognition techniques. This paper will summarize the current mainstream facial recognition technology methods. The basic ideas, methods, implementations, and corresponding drawbacks of current facial recognition methods are in this paper. The future trends of facial recognition and liveness detection are also discussed and concluded
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