Authentication Login E-Library with Multimodal Biometrics System - Pandapotan Siagian; Kurniabudi .; Erick Fernando; Herry Mulyono (IAES Indonesia Section, 2014)
Previous studies with the title of the login authentication e-library with method of CBIR for matching face, have proved reaching the level of accuracy about 75%. This multiple verification of QR-code/QR-CMBS this process data among other things the identity ID, fingerprint patterns and pattern signatures. Each user can have a QR-CMBS, which is used to login to the e-library. This research-oriented system development with application authentication login with QR code/QR-QR, Data of the CMBS will store data from bineri identity ID, fingerprint patterns and pattern signatures.The advantage of Retrieval CBIR is the popularity and test result with a high degree of accuracy and time parameters. The results obtained from QR-CMBS every training, i.e. classify and determine the value of fingerprint patterns and signatures for each label. Feature extraction results are temporarily stored in the session database and compare the features that are stored in the database image classification. The most similar classification results will be displayed, i.e. QR-CMBS, fingerprints and signatures, as well as verification of login. The application login authentication system of e-library uses to calculate the similarity of this research, will be able to extract the feature of colour, texture and edge of a multiple verification of QR-code/ QR-CMBS, fingerprint and signature by using the Prewitt gradient. The result of the extraction process feature is then used by the software in the learning process and calculates the similarity. Learning image contained in 3 classes features a picture that is stored in the database query 100 png images and the image of the sample test with the size 400 x 400. The results showed that the combination of the Prewitt filter extraction gradient magnitude. Verification data classification compared to the three classes, namely QR-CMBS, fingerprints and signatures contained in the database. Response time to find the most CMBS-QR is similar to 10 sample data, giving the effect of a higher degree of accuracy that is 97%.