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
-Feature point calculation、selection、Feature vector generation
As you can see, parallelcomputing is essential to solving such a problem.
Images taken from visual band are formed due to
Recently, face recognition on thermal/infrared
spectrum has gained popularity because thermal
images are formed due to emission not reflection.
Some of the commonly used face recognition techniques are Principal Component Analysis (PCA) , Linear Discriminate Analysis (LDA) and Gabor Filtering technique.
For point (X, Y), filter response denoted as
R is defined as
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 of
In a particular window of size SxT around
which the behavior or response of Gabor filter
kernel is maximum, as feature point.
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.
Feature vectors are generated at feature points as
discussed in previous sections. pth feature vector
of ith reference face is defined as:
where Wv and WT denote weight factors for the
matching scores of visual and thermal modules.
In this paper, Wv=WT=0.5
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.
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.