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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. 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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  1. Real-Time Efficient Parallel Thermal and Visual Face Recognition Fusion 2009/12/24 陳冠宇

  2. 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

  3. 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.

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

  5. 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.

  6. 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.

  7. Gabor Filtering For Face Recognition 1.Feature point calculation For point (X, Y), filter response denoted as R is defined as

  8. 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.

  9. 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.

  10. 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.

  11. 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:

  12. 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

  13. 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.

  14. Parallel Architecture For Face Recognition

  15. Limitations And Benefits • Complexity • Resource Requirements • Speedups • Portability

  16. Conclusions

  17. 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.

  18. Thank you

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