5 research outputs found
Quadratic Projection Based Feature Extraction with Its Application to Biometric Recognition
This paper presents a novel quadratic projection based feature extraction
framework, where a set of quadratic matrices is learned to distinguish each
class from all other classes. We formulate quadratic matrix learning (QML) as a
standard semidefinite programming (SDP) problem. However, the con- ventional
interior-point SDP solvers do not scale well to the problem of QML for
high-dimensional data. To solve the scalability of QML, we develop an efficient
algorithm, termed DualQML, based on the Lagrange duality theory, to extract
nonlinear features. To evaluate the feasibility and effectiveness of the
proposed framework, we conduct extensive experiments on biometric recognition.
Experimental results on three representative biometric recogni- tion tasks,
including face, palmprint, and ear recognition, demonstrate the superiority of
the DualQML-based feature extraction algorithm compared to the current
state-of-the-art algorithm
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