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Department of Automation Engineering National Formosa University. Attendance Administrative System Using Dactyloscopy. Kuang-Chyi Lee, Gavin Thomson and Yong-Jia Huang. Department of Automation Engineering, National Formosa University. Outline. Introduction Attendance administrative system
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Department of Automation EngineeringNational Formosa University Attendance Administrative System Using Dactyloscopy Kuang-Chyi Lee, Gavin Thomson and Yong-Jia Huang
Department of Automation Engineering, National Formosa University Outline • Introduction • Attendance administrative system • Identification of fingerprints • Discussions • Conclusions
Department of Automation Engineering, National Formosa University Introduction • Biometry, safety, uniqueness. • Attendance administrative system, database consistency.
4.Check out • 2. Check the recording of fingerprint data • 3.Collecting check in/out times Department of Automation Engineering, National Formosa University Attendance administrative system 1. Basic staff data
Department of Automation Engineering, National Formosa University Flowchart
Department of Automation Engineering, National Formosa University Functions • Check fingerprint • Personnel ID verification • Login/logout • Add/Delete staff • Modify personnel database • Report list • Administrator
Department of Automation Engineering, National Formosa University Database
Department of Automation Engineering, National Formosa University Fingerprint registration Select mode Fingerprint image Staff name Registered finger
Check mode Message Department of Automation Engineering, National Formosa University Fingerprint identification
Department of Automation Engineering, National Formosa University New staff
Department of Automation Engineering, National Formosa University ID verification
Login Logout Exit Data modification Department of Automation Engineering, National Formosa University Login system
Department of Automation Engineering, National Formosa University Records of attendance
Department of Automation Engineering, National Formosa University Administrator
Department of Automation Engineering, National Formosa University Identification of fingerprints fingerprint scanner
Department of Automation Engineering, National Formosa University Low-pass filter, Binarize, Thinning.
Department of Automation Engineering, National Formosa University Fingerprint classification (Henry [1])
Department of Automation Engineering, National Formosa University Fingerprint tracing Spherical algorithm Ci+1 is node for the first round window and ridge line
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..
Department of Automation Engineering, National Formosa University Simplified R-D Model p+α+tl+a+ p+α+tl+a+ p+α+tl+a+ p+α+tl+a+ p+α+tl+a+ p+α+tl+a+ p+α+tl+a+ p+α+tl+a+
Department of Automation Engineering, National Formosa University Fingerprint test
Department of Automation Engineering, National Formosa University 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 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 Thanks for your attention
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
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.
Binarize • If f(x,y)>m, then f(x,y) is255 • If f(x,y)< m, then f(x,y) is0 （ m is threshold value, f is input vision）
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
Smile ridge and Triangle ridge Triangle: Left Ending belong left area, Right Ending belong right area, the max is> . Smile ridge: Left Ending belong left area, Right Ending belong right area, and H<-HT. ( define 50°)
Left-loop ridge and Right-loop ridge Left-Loop: Left Ending and Right Ending in left area. Right-Loop: all ending in right area. Explain : main point are two point
Circle ridge and Balloon ridge Balloon ridge: Left end or Right End have node with ridge line. Circle ridge: Left End =Right End
Whorl ridge and Double-loop ridge Double-Loop ridge: <360° >360° Whorl ridge: Return
Ridge line trace spherical algorithm Finish trace result Ci+1 is node for the first round window and ridge line
Fragment point and Ending point Ending point Fragment elimination by additional tracing steps Return
