Open AccessComputational Intelligence for Biometric Applications: a Survey
Author(s) -
Ruggero Donida Labati,
Angelo Genovese,
Enrique Muñoz,
Vincenzo Piuri,
Fabio Scotti,
Gianluca Sforza
Publication year - 2016
Publication title -
computing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.184
H-Index - 11
eISSN - 2312-5381
pISSN - 1727-6209
DOI - 10.47839/ijc.15.1.829
Subject(s) - biometrics , computer science , normalization (sociology) , computational intelligence , artificial intelligence , machine learning , feature extraction , computational complexity theory , context (archaeology) , pattern recognition (psychology) , data mining , paleontology , algorithm , sociology , anthropology , biology
Biometric systems consist of devices, procedures, and algorithms used to recognize people based on their physiological or behavioral features, known as biometric traits. Computational intelligence (CI) approaches are widely adopted in establishing identity based on biometrics and also to overcome non-idealities typically present in the samples. Typical areas include sample enhancement, feature extraction, classification, indexing, fusion, normalization, and anti-spoofing. In this context, computational intelligence plays an important role in performing of complex non-linear computations by creating models from the training data. These approaches are based on supervised as well as unsupervised training techniques. This work presents computational intelligence techniques applied to biometrics, from both a theoretical and an application point of view.