Iris recognition is one of the most promising fields in biometrics. Notwithstanding this, there are not so many research works addressing it by machine learning techniques. In this survey, we especially focus on recognition, and leave the detection and feature extraction problems in the background. However, the kind of features used to code the iris pattern may significantly influence the complexity of the methods and their performance. In other words, complexity affects learning, and iris patterns require relatively complex feature vectors, even if their size can be optimized. A cross-comparison of these two parameters, feature complexity vs. learning effectiveness, in the context of different learning algorithms, would require an unbiased common benchmark. Moreover, at present it is still very difficult to reproduce techniques and experiments due to the lack of either sufficient implementation details or reliable shared code.

Iris recognition through machine learning techniques: A survey

RICCIARDI, STEFANO
2016-01-01

Abstract

Iris recognition is one of the most promising fields in biometrics. Notwithstanding this, there are not so many research works addressing it by machine learning techniques. In this survey, we especially focus on recognition, and leave the detection and feature extraction problems in the background. However, the kind of features used to code the iris pattern may significantly influence the complexity of the methods and their performance. In other words, complexity affects learning, and iris patterns require relatively complex feature vectors, even if their size can be optimized. A cross-comparison of these two parameters, feature complexity vs. learning effectiveness, in the context of different learning algorithms, would require an unbiased common benchmark. Moreover, at present it is still very difficult to reproduce techniques and experiments due to the lack of either sufficient implementation details or reliable shared code.
http://www.journals.elsevier.com/pattern-recognition-letters/
File in questo prodotto:
Non ci sono file associati a questo prodotto.

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11695/59786
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 96
  • ???jsp.display-item.citation.isi??? 64
social impact