5,875 research outputs found
A comparative study on face recognition techniques and neural network
In modern times, face recognition has become one of the key aspects of
computer vision. There are at least two reasons for this trend; the first is
the commercial and law enforcement applications, and the second is the
availability of feasible technologies after years of research. Due to the very
nature of the problem, computer scientists, neuro-scientists and psychologists
all share a keen interest in this field. In plain words, it is a computer
application for automatically identifying a person from a still image or video
frame. One of the ways to accomplish this is by comparing selected features
from the image and a facial database. There are hundreds if not thousand
factors associated with this. In this paper some of the most common techniques
available including applications of neural network in facial recognition are
studied and compared with respect to their performance.Comment: 8 page
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
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