real-time efficient parallel thermal and visual face recognition fusion
DESCRIPTION
Real-Time Efficient Parallel Thermal and Visual Face Recognition Fusion. 2009/12/24 陳冠宇. Outline. I ntroduction G abor F iltering F or F ace Recognition -Feature point calculation 、 selection 、 Feature vector generation -Similarity calculations - PowerPoint PPT PresentationTRANSCRIPT
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Real-Time Efficient Parallel Thermal and
Visual Face Recognition Fusion
2009/12/24
陳冠宇
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Outline
Introduction Gabor Filtering For Face Recognition
-Feature point calculation 、 selection 、 Feature vector generation
-Similarity calculations
Parallel Architecture For Face Recognition Limitations And Benefits Conclusions
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Introduction
Computer vision has long fascinated applications in psychology, neural science, computer science, and engineering.
A simple feature extraction algorithm may require thousands of basic operations per pixel.
As you can see, parallel computing is essential to solving such a problem.
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Introduction
This paper would discuss Task Parallel processing for fast face recognition system based on Gabor Filtering technique.
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Related work in Face Recognition
Images taken from visual band are formed due to
reflectance.
Recently, face recognition on thermal/infrared
spectrum has gained popularity because thermal
images are formed due to emission not reflection.
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Related work in Face Recognition
Some of the commonly used face recognition techniques are Principal Component Analysis (PCA) , Linear Discriminate Analysis (LDA) and Gabor Filtering technique.
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Gabor Filtering For Face Recognition 1.Feature point calculation
For point (X, Y), filter response denoted as
R is defined as
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Feature point calculation
Where σX and σY are the standard deviation of the Gaussian envelop along the x and y dimensions respectively.λ, θ and n are the wavelength, orientation and no oforientations respectively.
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2.Feature point selection
In a particular window of size SxT around
which the behavior or response of Gabor filter
kernel is maximum, as feature point.
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Feature point selection
Feature point located at any point can be evaluated as
Where Rj is the response of the image to the jth Gabor filter and C is any window.
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3.Feature vector generation
Feature vectors are generated at feature points as
discussed in previous sections. pth feature vector
of ith reference face is defined as:
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Decision Fusion Architectures
where Wv and WT denote weight factors for the matching scores of visual and thermal modules.
In this paper, Wv=WT=0.5
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Parallel Architecture For Face Recognition
As same face recognition steps are repeated for
visual, thermal and fused image. So it is proposed
that three individual face recognition processes for
each data be carried out on different slave computers.
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Parallel Architecture For Face Recognition
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Limitations And Benefits
Complexity Resource Requirements Speedups Portability
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Conclusions
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Conclusions
This paper briefly described a parallel design
framework for efficient and real-time face
recognition system. It defines new frontiers for
fast and efficient recognition system.
With our design framework, the realtime performance
can be achieved on regular computers,such as those
found in a student cluster.
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Thank you