face recognition system using LBP

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  • hii...could you please mail me the code for this project.. :)
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face recognition system using LBP

  1. 1. Final Year Project Face Recognition Using Local Features Marwan Noman 1051108336
  2. 2. Implementation Plan <ul><li>Milestones of Phase II: </li></ul>
  3. 3. Solution <ul><li>- Enrollment Process: </li></ul><ul><li>1. The images were enrolled by applying LBP function. </li></ul><ul><li>2. The histograms of these images will be stored as vectors in a matrix. </li></ul><ul><li>3. The distance function will compare two matrixes from the same dimensionality to get matching image. </li></ul>
  4. 4. Solution (2) <ul><li>- Enrollment Process: </li></ul>
  5. 5. Solution (3) <ul><li>Feature Extraction Algorithm: </li></ul>
  6. 6. Solution (4) <ul><li>-Matching Algorithm: </li></ul>
  7. 7. Implementation Process <ul><li>Extracted Histogram: </li></ul>
  8. 8. Implementation Process (2) <ul><li>The LBP is used to extract the local important features of the images. </li></ul><ul><li>Hence, it will convert the result to histogram values. </li></ul><ul><li>These values will be stored in a matrix as values. </li></ul>
  9. 9. Implementation Process (3) <ul><li>- Enrollment and Recognition processes : </li></ul><ul><li>- There are around 96 images of different subjects. </li></ul><ul><li>- There are 12 different subjects. Each subject has 8 different images. </li></ul><ul><li>- An image will be used of each subject for recognition purposes. </li></ul>
  10. 10. Implementation Process (4) <ul><li>- Basic Design: </li></ul>
  11. 11. Implementation Process (5) <ul><li>- Finalized Design: </li></ul>
  12. 12. Implementation Process (6) <ul><li>- Loading the image : </li></ul>
  13. 13. Implementation Process (7) <ul><li>- Extracting the image: </li></ul>
  14. 14. Implementation Process (8) <ul><li>- Scanning the image: </li></ul>
  15. 15. Testing <ul><li>- Low quality images: </li></ul>
  16. 16. Testing (2) <ul><li>- False Matching: </li></ul>
  17. 17. Testing (3) - Positive Matching:
  18. 18. Conclusion <ul><li>- Great experience of Face Recognition systems . </li></ul><ul><li>- Achievement of a good level of understanding LBP algorithm, and it was a good opportunity for me to distinguish between local features and global features of images . </li></ul><ul><li>- One of the problems was faced during the testing, is image quality. </li></ul>
  19. 19. Conclusion (2) <ul><li>- Euclidean Distance was the solution by finding the distance between histograms of the original image and the enrolled images . </li></ul><ul><li>- By the minimum distance between the histograms, it will find the most matching image of the original one. </li></ul><ul><li>- It is compulsory to crop bad quality images . </li></ul>
  20. 20. <ul><li>The End </li></ul>

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