International Journal of Engineering Trends and Technology (IJETT) – Volume 8 Number 3- Feb 2014
Survey of Face Recognition Using Super-Resolution
Techniques
L Gershom Winston#1, Jemima Jebaseeli*2
#
P.G Scholar, Department of Information Technology, Karunya University
Coimbatore, India
Abstract— The face recognition techniques having vital role in
the area of computer science and technology. Many image
resolution techniques have been implemented to make the
automatic face recognition operations efficient. The link
between the face recognition and image resolution efficiency
have not being paid much attention. The relationship is probed
consistently and based on the experimental outcomes from
these conventional ideas comparison is performed. Processing
time will be larger to register the images with low resolution.
Concepts adopted global motion models are ways to improve
the performance. Many parameter validations are needed to be
done, over which these algorithms furnish comparatively good
recognition outcomes than images that are diagnosed through
the conventional methods.
Keywords— Eigen Faces, Face recognition, Graph Matching.
I. INTRODUCTION
Human faces in surveillance video possess
characteristics that are inherently difficult to accommodate
in this domain. Some of these problems include nonplanarity, non-rigidity and self-occlusion and reflectance
variation. Automatic face recognition has become an
important part of user authentication and security
infrastructure in recent years. Most super-resolution methods
simplify the problem by restricting the correspondence
between low-resolution frames to parametric global motion,
thus reducing the dimensionality of the problem. Superresolution is an ill-posed inverse problem, in that the number
of possible solutions given the available data is too large.
This is adequate for satellite imagery or other situations
where the motion is known to be global. Surveillance
footage however, consists of independently moving nonrigid objects such as faces. Due to its non-intrusive nature, it
can be used to identify subjects from surveillance video
without user cooperation. Existing face recognition systems
perform poorly with surveillance imagery because of intraclass variations introduced by uncontrolled lighting, pose
variations and poor resolution.
II. METHODOLOGY:
The possibility of face image recognition is done with
the use of Super-Resolution technique. Investigation
restoration super resolving Gaussian resolution has been
employed here [1-5]. Face images have been identified here
with certain limitations of the face images, etc. The concept
of face recognition is getting more novel techniques day by
ISSN: 2231-5381
day. Super Resolution technique is employed here to
overcome the drawbacks of rejoining the optical flow in the
gratuitous information. Maximum Likelihood Estimator tells
us about the limitation and the restoration of the sampled
images. Gaussian Progression [3] and the optical flow
method [5] tell us about the identification of the high resolute
image. The following diagnosed papers tell us about the
algorithm of finding out the face images through face
recognition.
A. Entire Investigation into Optical Flow Super-Resolution
(SR) for Surveillance Applications
Super-resolution (SR) is a technique that can
overcome the limitation by combining gratuitous
information from several frames of a video to produce
better resolution images. A problem that besets many
existing Super Resolution systems is that they can only
conduct simple, rigid inter-frame transformations, thus
executing with face images as faces are non-rigid, nonplanar and Non-Lambertian in nature. Features of these
techniques are: 1) Due to frame separation, we can
easily separate and identify the face image.2) Upsampling and down-sampling exactly gives the
separated frame sequence. Limitations of these
techniques are: 1) Reconstruction is found to be time
consuming, 2) interpolation and under-construction of
the image depends upon the nature of the face image.
B. Restoration of a Single Super-resolution Image from
Several Blurred, Noisy, under-sampled Measured
Images.
Maximum Likelihood Estimator, Maximum
Posteriori Probability Estimator, and the theoretic
approach using Projection onto Convex Sets (POCS)
are the basic ideas which were given for under-sampling
of the face images. The proposed approach is general
but accepts explicit cognition of the time-variant and
linear space blur, the (additive Gaussian) noise. A new
combination gives the simplicity of the Maximum
Likelihood and the incorporation of non-ellipsoid
restraints are presented, giving improved restoration
performance. The hybrid technique is shown to meet the
only solution of a new identification. For these
processes, Super-resolution refurbishment from stand-
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International Journal of Engineering Trends and Technology (IJETT) – Volume 8 Number 3- Feb 2014
still measurements is also derived. Features of this
algorithm includes: 1) the algorithm supports motion
free estimation and detection 2) measurement is done
through separating regularized and non-regularized
regions for frequency domain analysis. Limitations of
this algorithm including 1) computational complexity in
deriving the mathematical model for hybrid restoration
method 2) techniques and algorithms are new to learn
so difficult in deriving the terms.
C. Single image super-resolution using Gaussian process
regression
Each pixel is anticipated by its neighbours by
Gaussian process regression. Using proper co-variance
identification, the Gaussian process fixation can
perform clustering of pixels depending on their local
structural elements. The demonstration of this algorithm
can derive the basic information comprised in a single
low-resolution signal to generate a high-resolution of
the same with sharp edges, which is comparatively
superior in quality and performance of other edgedirected resolution algorithms and other techniques.
Features of this algorithm including 1) Gaussian tool
reduces noise as much as possible2) the co-variance and
super-resolution values give us the exact image located.
Limitations of this algorithm including 1) the Gaussian
Progression (GP) values only supports the mapping
ranges 2) reconstruction and performance are little bit
moderate as compared.
E. Super-Resolved Face Images using Robust Optical
Flow
Biometrics such as hand sign recognition, face
recognition, Gesture recognition based research is a fast
moving field because of the drastic demand on
identification or authentication of an individual.
Somehow, only identification documents can be
identified. Super-resolution algorithm has undergone a
great success in a wide variety such as satellite imagery,
medical imaging and some pattern recognition
applications. Rather, biometrics has revealed a wide
range of human-based features and characteristics
which will be useful to authenticate an individual.
Features of these algorithm includes: 1) Illumination
can be avoided and motion compensation can be done
2) De-blurring is achieved at a concern and
authentication is done with high efficiency. Limitations
of this algorithm including: 1) interpolation for filter
techniques and face identification due to interpolation
makes pixel identification complex 2) Biometric
identification is the oldest idea and super resolution
techniques for all the biometric are not supported.
D. The Role of Motion Models in Super-Resolving
Surveillance Video for Face Recognition
Though the application of super-resolution
techniques has showed the power to make face
recognition efficient, they are difficult to be
implemented for real-time applications due to their
complexity and computational complexity. As drastic
portion of working time is given to low resolution
images, techniques have adopted global motion
examples to make them efficient. The retreat of many
global features is that they cannot adapt the complex
local features, say, multi-objects moving and tracking,
static or dynamic background that presents in a
surveillance conditions. Features of these techniques
including 1) Eigen face approach gives us the accurate
identification of the face images comparatively 2) The
standardized implementations can be given for many
research databases using this technique. Limitations of
this technique are there is no much explanation about
the concept of super resolution in motion surveillance.
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International Journal of Engineering Trends and Technology (IJETT) – Volume 8 Number 3- Feb 2014
TABLE I
COMPARISON TABLE
Investigation into
Optical Flow
Super-Resolution
(SR) for
Surveillance
Applications
Restoration of a Single
Super-resolution
Image from Several
Blurred, Noisy, undersampled Measured
Images
Single Image SuperResolution using
Gaussian Process
Regression
The Role of
Motion Models in
Super-Resolving
Surveillance Video
for Face
Recognition
Super-Resolved
Face Images
using Robust
Optical Flow
Face image
acquisition
Super-Resolution
Image restoration
Recognition through
Super Resolution
Gaussian Process
Progression
Multi-object
tracking
Eigen Face
approach
Biometric
Recognition
Interpolation
Methodology
Super resolution
using motion
compensation
Maximum Likelihood
Estimator
Gaussian Progression
and fixation
Eigen values and
Eigen face detection
Algorithm
Super Resolution
with frame
separation
Super Resolution and
Restoration
Gaussian fixation
Frame separation
and Super
Resolution through
conventional
algorithm
Biometric through
motion
compensation and
interpolation
Registration and
Restoration
Input
Surveillance Image
Noisy Image
Single Dimensional
Image
Eigen Face Images
Video/Frame
separated Image
Output
Recognized face
image
Enhanced Image
High Resolution
Image
Multi face images
Recognized multi
faces
Factors affects
detection and
extraction
Interpolation
disturbs the faces
Problem in restoration –
blurring occurs slightly
Gaussian Progression
leads to less
concentration in
reconstruction
Super resolution in
motion surveillance
has some defects
Pixel identification
makes complex
Goal
Approach
Maximum Likelihood
III. CONCLUSIONS
A suitable Super-Resolution (SR) system gives us a little
borderline improvement over interpolation in the absence of
noise. More additional techniques can be gained and realized
when the images are noisy, as the frame-fusion process
helps to decrease the noise level. The super resolution
algorithm is not that much robust to adapt the localization
errors.
ISSN: 2231-5381
REFERENCES
[1]
[2]
Fookes, A. Maeder, Comparison of popular non-rigid image
registration techniques and a new hybrid mutual information-based
fluid algorithm, in: Proceedings of the APRS Workshop on Digital
Image Computing, 2003.
B. Gunturk, A. Batur, Y. Altunbasak, M.H. III, R. Mersereau, Eigenface-domain super-resolution for face recognition, IEEE
Transactions on Image Processing 12 (2003) 597–606. M.
Wegmuller, J. P. von der Weid, P. Oberson, and N. Gisin, “High
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International Journal of Engineering Trends and Technology (IJETT) – Volume 8 Number 3- Feb 2014
[3]
[4]
resolution fiber distributed measurements with coherent OFDR,” in
Proc. ECOC’00, 2000, paper 11.3.4, p. 109.
A. Lemieux, M. Parizeau, Experiments on Eigen-faces robustness,
in: Proceedings of ICPR-2002, vol. 1, 2002
F. Lin, C. Fookes, V. Chandran, S. Sridharan, Investigation into
optical flow super-resolution for surveillance applications, in:
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[5]
[6]
Proceedings of APRS Workshop on Digital Image Computing 2005,
2005.
E. Bailly-Bailliere, S. Bengio, K. Messer, The BANCA database and
evaluation protocol, in: Proceedings of AVBPA-2003
S. Baker, T. Kanade, Super Resolution Optical Flow, Tech. Rep.
CMU-RI-TR-99- 36, The Robotics Institute, Carnegie Mellon
University, October 1999.
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