Autor/Autorin: Kadam Pooja Shree
Mrs. Pooja Shree Kadam completed ME in computer engineering from sigma institute of engineering and technology Vadodara, BE from Vadodara Institute of Engineering, Kotambi, waghodia. She is currently working as temporary lecturer at Polytechnic, and as a visiting faculty at, BBA, of the Maharaja Sayajirao University, Vadodara, India.
This book includes a fingerprint indexing scheme at feature level fusion based on Minutiae Vicinity and Minutiae Cylindrical Code and the classifying them using the SVM and K-means classifier, proving which is more efficient, which will improve the efficiency in seeking a candidate reference list from large scale biometric data databases, where the personality related with the input data is dictated by contrasting it with each and every entry in the database. This coordinating procedure is tedious and conceivably increments, the rate of wrong identification, hence we propose a new fingerprint indexing approach which would improve the performance of the system.
Kadam, Pooja Shree
Mrs. Pooja Shree Kadam completed ME in computer engineering from sigma institute of engineering and technology Vadodara, BE from Vadodara Institute of Engineering, Kotambi, waghodia. She is currently working as temporary lecturer at Polytechnic, and as a visiting faculty at, BBA, of the Maharaja Sayajirao University, Vadodara, India.
Über den Autor
Mrs. Pooja Shree Kadam completed ME in computer engineering from sigma institute of engineering and technology Vadodara, BE from Vadodara Institute of Engineering, Kotambi, waghodia. She is currently working as temporary lecturer at Polytechnic, and as a visiting faculty at, BBA, of the Maharaja Sayajirao University, Vadodara, India.
Klappentext
This book includes a fingerprint indexing scheme at feature level fusion based on Minutiae Vicinity and Minutiae Cylindrical Code and the classifying them using the SVM and K-means classifier, proving which is more efficient, which will improve the efficiency in seeking a candidate reference list from large scale biometric data databases, where the personality related with the input data is dictated by contrasting it with each and every entry in the database. This coordinating procedure is tedious and conceivably increments, the rate of wrong identification, hence we propose a new fingerprint indexing approach which would improve the performance of the system.
