Publications

@inproceedings{abateSKIPSOMSkewnessAmp2016,

title = {SKIPSOM: Skewness Amp; Kurtosis of Iris Pixels in Self Organizing Maps for Iris Recognition on Mobile Devices},

author = { Andrea Abate and Silvio Barra and Luigi Gallo and Fabio Narducci},

doi = {10.1109/ICPR.2016.7899625},

year  = {2016},

date = {2016-12-01},

booktitle = {2016 23rd International Conference on Pattern Recognition (ICPR)},

pages = {155--159},

abstract = {In the last fifteen years, smartphones have become very popular amongst the population, with the subsequent development of dozens of applications aimed at providing security to these portable devices. Nowadays, the cutting edge devices are also provided with biometric sensors (e.g., fingerprint sensors) allowing the users to access them without using the out-of-date alphanumerical password. In this work, we present a method that realizes iris recognition by means of Self Organizing Maps (SOM). In order to obtain a better refined and discriminative feature map, the RGB data of the iris, previously segmented, have been combined with two statistical descriptors. The algorithm has been designed specifically to require a low processing power, making it an ideal choice in the context of mobile devices.},

keywords = {Image segmentation, Iris, Mobile handsets, Self-organizing feature maps},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{abate_skipsom_2016,

title = {SKIPSOM: Skewness amp; kurtosis of iris pixels in Self Organizing Maps for iris recognition on mobile devices},

author = {Andrea Abate and Silvio Barra and Luigi Gallo and Fabio Narducci},

doi = {10.1109/ICPR.2016.7899625},

year  = {2016},

date = {2016-12-01},

booktitle = {2016 23rd International Conference on Pattern Recognition (ICPR)},

pages = {155–159},

abstract = {In the last fifteen years, smartphones have become very popular amongst the population, with the subsequent development of dozens of applications aimed at providing security to these portable devices. Nowadays, the cutting edge devices are also provided with biometric sensors (e.g., fingerprint sensors) allowing the users to access them without using the out-of-date alphanumerical password. In this work, we present a method that realizes iris recognition by means of Self Organizing Maps (SOM). In order to obtain a better refined and discriminative feature map, the RGB data of the iris, previously segmented, have been combined with two statistical descriptors. The algorithm has been designed specifically to require a low processing power, making it an ideal choice in the context of mobile devices.},

keywords = {Image segmentation, Iris, Mobile handsets, Self-organizing feature maps},

pubstate = {published},

tppubtype = {inproceedings}

}