02157nam a22001577a 4500999001700000100006900017245007600086260002700162300000900189500151400198700004201712856003201754942001101786952010001797952010201897  c65487d65484  aBhatti, Kamran Ali a15MSSE04aSupervisor- Sajida Parveen Soomro  aFingerprint Liveness Detection Using Dynamic Local Ternary Pattern DLTP  aNawabshahbQUESTc2019  a40p.  aABSTRACT


These days biometric confirmation frameworks are utilized for security applications like  check  and  recognizable  proof.  There  are  many  biometric	systems  like fingerprint, face, and iris. Biometric system is better than pi n and password based system because they can be easily stolen or forgotten bu t i n biometric system can't easily steal and forgot because the characteristics of biometric systems are unique. J n biometric  system,  the  fingerprint  biometric  system  is  mostly  used  for  security purpose to identify the users according to the uniqueness of the iris fingerprint but still some  problem  occurs  in  fingerprint  biometric  security  systems  like  some  fake reproducible fingerprints made by silicon or other material, the fingerprints made from these materials can unlock the original fingerprint impression. I t i s viewed a one of the noticeable problem in fingerprint systems.
There is needs to develop an algorithm i n fingerprint liveness detection to identify the user either the user is genuine or fake one. So a new software based met hod Dynamic Local Ternary Pattern (DLTP) is proposed for fingerprint liveness detection with Weber's Law in this research. The Support Vector Machine (SVM) Classifier is used for fake detect ion. The results obtained from the proposed method (DLTP) texture descriptor attained optimum accuracy and clearly outperformed the reported  Local Binary  Pattern  (LBP) and  Local  Ternary  Pattern  (LTP) texture
descriptors.  aDepartment of Information Technology   uhttp://tinyurl.com/4wmr7wcy  cTHESIS  00104070aRESEARCHbRESEARCHd2019-09-30l0oR/IMS-19pMP/46-539r2019-09-30 00:00:00yTHESIS  00104070aRESEARCHbRESEARCHd2023-12-14l0pMP/54-669r2023-12-14 00:00:00w2023-12-14yTHESIS