Real time efficient parallel thermal and visual face recognition fusion
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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

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Real-Time Efficient Parallel Thermal and Visual Face Recognition Fusion

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Real time efficient parallel thermal and visual face recognition fusion

Real-Time Efficient Parallel Thermal and Visual Face Recognition Fusion

2009/12/24

陳冠宇


Outline

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


I ntroduction

Introduction

  • Computer vision has long fascinatedapplications in psychology, neural science, computerscience, and engineering.

  • A simple featureextractionalgorithm may require thousands of basic operationsper pixel.

    As you can see, parallelcomputing is essential to solving such a problem.


I ntroduction1

Introduction

  • This paper would discuss Task Parallel processing for fast face recognition system based on Gabor Filtering technique.


Related work in face recognition

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.


Related work in face recognition1

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.


G abor f iltering f or f ace recognition 1 feature point calculation

Gabor Filtering For Face Recognition 1.Feature point calculation

For point (X, Y), filter response denoted as

R is defined as


Feature point calculation

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 of

orientations respectively.


2 feature point selection

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.


Feature point selection

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.


3 feature vector generation

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:


Decision fusion architectures

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


Parallel architecture for face recognition

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.


Parallel architecture for face recognition1

Parallel Architecture For Face Recognition


Limitations and benefits

Limitations And Benefits

  • Complexity

  • Resource Requirements

  • Speedups

  • Portability


Conclusions

Conclusions


Conclusions1

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.


Thank you

Thank you


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