The IRMA Community
Newsletters
Research IRM
Click a keyword to search titles using our InfoSci-OnDemand powered search:
|
Machine Learning for Biometrics
Abstract
Biometrics aims at reliable and robust identification of humans from their personal traits, mainly for security and authentication purposes, but also for identifying and tracking the users of smarter applications. Frequently considered modalities are fingerprint, face, iris, palmprint and voice, but there are many other possible biometrics, including gait, ear image, retina, DNA, and even behaviours. This chapter presents a survey of machine learning methods used for biometrics applications, and identifies relevant research issues. The author focuses on three areas of interest: offline methods for biometric template construction and recognition, information fusion methods for integrating multiple biometrics to obtain robust results, and methods for dealing with temporal information. By introducing exemplary and influential machine learning approaches in the context of specific biometrics applications, the author hopes to provide the reader with the means to create novel machine learning solutions to challenging biometrics problems.
Related Content
|
Muhammad Naeem, Salman Memon, Anita Larik, Syed Rizwan Mehdi, Hasan Ahmed Faridi, Khalida Khan, Sana Zafar, Manoj Kumar.
© 2026.
20 pages.
|
|
Imdad Ali Shah, N. Z. Jhanjhi.
© 2026.
12 pages.
|
|
Hafsa Muzammal, Muhammad Zaman, Muhammad Safdar, Muhammad Adnan Shahid, Zuhaib Nishtar, Muhammad Bilal, Muntaha Munir, Mehar Muhammad Haseeb, Aamir Raza, Syed Intsar Hussain Shah, Usman Zafar, Nalain E. Muhammad, Hafiz Muhammad Bilawal Akram.
© 2026.
30 pages.
|
|
Luminita Diaconu, Yassine Mouniane.
© 2026.
32 pages.
|
|
Kumar J. Parmar, Tejas Chandulal Chauhan, T. Premavathi.
© 2026.
32 pages.
|
|
Mahmoud Oudghiri, Mohamed El Bakkali, Yassine Mouniane, Nagla Abid, Samah Bouhassoun, Fatima-ezzahra Jaayefar, Fath Alah Elwahab, Issam El-Khadir, Ahmed Chriqui, Mohammed Ibriz.
© 2026.
26 pages.
|
|
Issam El-Khadir, Yassine Mouniane, Ahmed Chriqui, Mohamed El Bakkali, Driss Hmouni.
© 2026.
34 pages.
|
|
|