1 / 36

Palmprint Classification

Palmprint Classification. People involved: Li Fang (Lecturer) Maylor Karhang Leung (Assoc Prof) Kean Fatt Choon (Final Year Project student). Task. Create a hierarchical system to improve the speed of palmprint recognition. Contents.

christmas
Download Presentation

Palmprint Classification

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Palmprint Classification People involved: Li Fang (Lecturer) Maylor Karhang Leung (Assoc Prof) Kean Fatt Choon (Final Year Project student)

  2. Task Create a hierarchical system to improve the speed of palmprint recognition

  3. Contents Victor - Introduction, Research, conventional process Tejas - Algorithm, explanation of various categories

  4. Introduction What is palmprint recognition? • Form of computer-aided personal recognition • Capturing images of palmprint and matching it with the database • Use for security purposes in many countries

  5. Definitions Introduction to principal lines • Life Line • Head Line • Heart Line

  6. Rationale Why palmprint? • Widely used by many security agencies. • Cost effective • Non-intrusive • Possible to build highly accurate biometric system

  7. Rationale Why others methods such as iris and fingerprint are not highly effective ? • Iris input devices are expensive. • Iris is intrusive • Fingerprint require high definition capturing devices. • Some may be finger deficient

  8. BEGIN Match with user’s registered palm print in the database? False True END Palmprint Capture Result Output Input Process Contains 1000 images Database

  9. Limitations • Image captured has to be matched with every single image in database • Time consuming • Too high computational complexity to be applicable

  10. Aims & Expectations • Our aim is to speed up this process by adding in 2 extra filters before the palm print is matched • We expect to increase the speed of the recognition which is one of the most deterring limitation

  11. Survey • Conducted a survey among people living in Singapore • Gender • Age • Nationality • Survey can be used in our study and design of algorithm which will suit the residents here.

  12. Survey Result From our survey, • The population palms can be classified into 6 categories (elaborated in the later slide) • Majority of the population lies in one category. • However, significant amount of the population still falls under the other categories

  13. Studies have shown... • According to the algorithm proposed on the research paper • The algorithm proposed categorizes the palmprints into 6 categories • Palm Categories

  14. Palm Categories Cat 3 Cat 2 Cat 1 Cat 6 Cat 4 Cat 5

  15. Result Algorithm proposed by the research paper

  16. Category 5

  17. New Algorithm Why a new algorithm is required? • 78% of the people lie in the 5th category • Based on the current system, the input image has to be matched with every image in the database before the result is obtained

  18. Input Palmprint Categories with the initial algorithm Belong to Category 5? Y Categories with the new algorithm N Image matching with the images in same category in data base Result Flowchart Input Palmprint Categories with the initial algorithm Belong to Category 5? Y Categories with the new algorithm N Image matching with the images in same category in data base Result

  19. Input Palmprint Categories with the initial algorithm Belong to Category 5? Y Categories with the new algorithm N Image matching with the images in same category in data base Result Flowchart

  20. Input Palmprint Categories with the initial algorithm Belong to Category 5? Y Categories with the new algorithm N Image matching with the images in same category in data base Result Flowchart In Cat. 1

  21. Input Palmprint Categories with the initial algorithm Belong to Category 5? Y Categories with the new algorithm N Image matching with the images in same category in data base Result Flowchart In Cat. 1 NO T CAT. 5

  22. Input Palmprint Categories with the initial algorithm Belong to Category 5? Y Categories with the new algorithm N Image matching with the images in same category in data base Result Flowchart Compare Not Cat 5 In Cat. 1 Result NO T CAT. 5

  23. Input Palmprint Categories with the initial algorithm Belong to Category 5? Y Categories with the new algorithm N Image matching with the images in same category in data base Result Flowchart

  24. Input Palmprint Categories with the initial algorithm Belong to Category 5? Y Categories with the new algorithm N Image matching with the images in same category in data base Result Flowchart In Cat. 5

  25. Input Palmprint Categories with the initial algorithm Belong to Category 5? Y Categories with the new algorithm N Image matching with the images in same category in data base Result Flowchart In Cat. 5 True

  26. Input Palmprint Categories with the initial algorithm Belong to Category 5? Y Categories with the new algorithm N Image matching with the images in same category in data base Result Flowchart In Cat. 5 E.g cat. A

  27. Input Palmprint Categories with the initial algorithm Belong to Category 5? Y Categories with the new algorithm N Image matching with the images in same category in data base Result Flowchart Compare Cat A In Cat. 5 Result Cat A.

  28. New Process Contains 1000 Images Cat A Cat B Cat C Palmprint Capture Result Cat D Cat E Not Cat 5 Input Database

  29. New Algorithm Step 1 • The first line connected from the end of the little finger to the intersection of the life line and head line (green line) • The second line is connected from the end of life line to intersection of life and head line (red line) • The third line is connected from point of intersection green line and heart line to midpoint of red line (purple line)

  30. New Algorithm Step 2 • Draw a triangle inside the triangle by connecting the mid points of the each line • Divide the two triangle into 4 parts as shown

  31. New Algorithm Step 3 • Draw a line from end of heart line to end of life line • Draw a line from beginning of heart line to the intersection of life line and head line • The location of the point of intersection of these 2 lines can then be used to categorize the palm

  32. Category B Category A Category D Implementation Category C Category E

  33. Result • We tried this algorithm on 100 subjects • The pie chart above shows the percentage of each category • It can be concluded that algorithm proposed is effective

  34. Summary • Our research showed that process of palmprint recognition is inefficient and can be improved • Our survey analysis revealed that most people lie in one particular category • Proposed a robust algorithm via study of characteristics of principal lines to reinforce the method of palmprint classification • Tried out the proposed algorithm on 100 subjects to investigate its effectiveness

  35. The End

More Related