1. Department of Automation Engineering National Formosa University Attendance Administrative System Using Dactyloscopy Kuang-Chyi Lee, Gavin Thomson and Yong-Jia Huang

2. Outline Introduction Attendance administrative system Attendance administrative system Identification of fingerprints Identification of fingerprints Discussions Discussions Conclusions Department of Automation Engineering, National Formosa University

3. Introduction  Biometry, safety, uniqueness.  Attendance administrative system, database consistency. Department of Automation Engineering, National Formosa University

4. 1. Basic staff data 2. Check the recording of fingerprint data 3. Collecting check in/out times 4. Check out Attendance administrative system

5. Flowchart Department of Automation Engineering, National Formosa University

6. Functions Check fingerprint Personnel ID verification Login/logout Add/Delete staff Modify personnel database Report list Administrator Department of Automation Engineering, National Formosa University

7. Database StaffS. Serial Number NameIndividual data P. Serial Number D. Serial Number PositionP. Serial Number PositionSalary D. Serial Number: D. Serial Number Department NameDepartment Location Attendan ce Form: S. Serial Number Inscroll DateInscroll Time Check in Time

8. Fingerprint image Fingerprint registration Registered finger Staff name Select mode Department of Automation Engineering, National Formosa University

9. Fingerprint identification Department of Automation Engineering, National Formosa University Check mode Message

10. New staff Department of Automation Engineering, National Formosa University

11. ID verification Department of Automation Engineering, National Formosa University

12. Login system Department of Automation Engineering, National Formosa University Login Exit Logout Data modification

13. Records of attendance Department of Automation Engineering, National Formosa University

14. Administrator Department of Automation Engineering, National Formosa University

15. Identification of fingerprints fingerprint scanner Department of Automation Engineering, National Formosa University

16. Low-pass filter, Binarize, Thinning.

17. Department of Automation Engineering, National Formosa University Fingerprint classification (Henry [1]) Plain Arch typeTented Arch typeLeft-loop typeRight-loop type Central Pocket whorl type Plain Whorl typeDouble Loop Whorl type accidenta l do not belong any one

18. Department of Automation Engineering, National Formosa University Fingerprint tracing Spherical algorithm C i+1 is node for the first round window and ridge line

19. Department of Automation Engineering, National Formosa University Ten ridge patterns (Chang [6]) Plain ridge: Left End belong to left area, right End belong to right area, |H|< fifty pixel..

20. Simplified R-D Model stylerule Plain Tented Right Left Central Plain Double p + a α ＊ t + a + P + α + tr + a + α ＊ s + α + (c + w) + α + a + p + α + b + α + a + α ＊ s + α + dd + α + α + p + α + tl + a + p+α+tl+a+p+α+tl+a+ Department of Automation Engineering, National Formosa University

21. Fingerprint test Department of Automation Engineering, National Formosa University

22. Discussions The FRR for the fingerprint system is 1.45%,and the FAR is 0.8%. If we add person date, it can cancellation FRR and reduce FAR. In this system, average time to identification is about 1.6 seconds. Department of Automation Engineering, National Formosa University

23. Conclusions The paper develop high exactly, low error, operation fast relation database by logic database. Finish a fingerprint and person ID to do attendance administrative system, it can make user to use easily. Finish the fingerprint classification by the R-D Mode after catch the fingerprint. Department of Automation Engineering, National Formosa University

24. Thanks for your attention Department of Automation Engineering, National Formosa University

25. Database normalization In first normalization can reduce the value for recover roll. And have three reasons are sample, easy, and can use an operation to success. In second normalization can reduce the depend on each other for data, and use division table can make it the same main key. In third normalization can reduce the depend on each other for data, it can make the data structure for delete or insert. Return

26. Low-pass filter It can use mask to process the value in one time. We can give the middle value for the average value in 3x3 space mask. 1/9

27. Binarize If f(x,y)>m, then f(x,y) is 255 If f(x,y)< m, then f(x,y) is 0 （ m is threshold value, f is input vision ）

28. Thinning Tinning algorithm can be division into two steps, and we will implement it until no delete point. Step1 delete condition ： (a) 2 ≦ N(P1) ≦ 6 (b) S(P1)=1 (c1) P2 ·P4 ·P6 = 0 (d1) P4 ·P6 ·P8 = 0 Step2 delete condition ： (a) 2 ≦ N(P1) ≦ 6 (b) S(P1)=1 (c2) P2 ·P4 ·P8 = 0 (d2) P2 ·P6 ·P8 = 0 Return

29. Smile ridge and Triangle ridge Smile ridge: Left Ending belong left area, Right Ending belong right area, and H<-H T. Triangle: Left Ending belong left area, Right Ending belong right area, the max is >. ( define 50° )

30. Left-loop ridge and Right-loop ridge Right-Loop : all ending in right area. Left-Loop: Left Ending and Right Ending in left area. Explain : main point are two point

31. Circle ridge and Balloon ridge Circle ridge: Left End =Right End Balloon ridge: Left end or Right End have node with ridge line.

32. Whorl ridge: Whorl ridge and Double-loop ridge >360° Double-Loop ridge : <360° Return

33. Ridge line trace Finish trace result spherical algorithm C i+1 is node for the first round window and ridge line

34. Fragment point and Ending point Fragment elimination by additional tracing steps Ending point Return