AHCI RESEARCH GROUP
Publications
Papers published in international journals,
proceedings of conferences, workshops and books.
OUR RESEARCH
Scientific Publications
How to
Here you can find the complete list of our publications.
You can use the tag cloud to select only the papers dealing with specific research topics.
You can expand the Abstract, Links and BibTex record for each paper.
You can use the tag cloud to select only the papers dealing with specific research topics.
You can expand the Abstract, Links and BibTex record for each paper.
2017
Abate, Andrea; Barra, Silvio; Gallo, Luigi; Narducci, Fabio
Kurtosis and Skewness at Pixel Level as Input for SOM Networks to Iris Recognition on Mobile Devices Journal Article
In: Pattern Recognition Letters, 2017, ISSN: 0167-8655.
Abstract | Links | BibTeX | Tags: Iris, Mobile Biometric Recognition, Statistical descriptors, Unsupervised Learning
@article{abateKurtosisSkewnessPixel2017,
title = {Kurtosis and Skewness at Pixel Level as Input for SOM Networks to Iris Recognition on Mobile Devices},
author = { Andrea Abate and Silvio Barra and Luigi Gallo and Fabio Narducci},
doi = {10.1016/j.patrec.2017.02.002},
issn = {0167-8655},
year = {2017},
date = {2017-02-01},
urldate = {2017-02-21},
journal = {Pattern Recognition Letters},
abstract = {The increasing popularity of smartphones amongst the population laid the basis for a wide range of applications aimed at security and privacy protection. Very modern mobile devices have recently demonstrated the feasibility of using a camera sensor to access the system without typing any alphanumerical password. In this work, we present a method that implements iris recognition in the visible spectrum through unsupervised learning by means of Self Organizing Maps (SOM). The proposed method uses a SOM network to cluster iris features at pixel level. The discriminative feature map is obtained by using RGB data of the iris combined with the statistical descriptors of kurtosis and skewness. An experimental analysis on MICHE-I and UBIRISv1 datasets demonstrates the strengths and weaknesses of the algorithm, which has been specifically designed to require low processing power in compliance with the limited capability of common mobile devices.},
keywords = {Iris, Mobile Biometric Recognition, Statistical descriptors, Unsupervised Learning},
pubstate = {published},
tppubtype = {article}
}
The increasing popularity of smartphones amongst the population laid the basis for a wide range of applications aimed at security and privacy protection. Very modern mobile devices have recently demonstrated the feasibility of using a camera sensor to access the system without typing any alphanumerical password. In this work, we present a method that implements iris recognition in the visible spectrum through unsupervised learning by means of Self Organizing Maps (SOM). The proposed method uses a SOM network to cluster iris features at pixel level. The discriminative feature map is obtained by using RGB data of the iris combined with the statistical descriptors of kurtosis and skewness. An experimental analysis on MICHE-I and UBIRISv1 datasets demonstrates the strengths and weaknesses of the algorithm, which has been specifically designed to require low processing power in compliance with the limited capability of common mobile devices.
Abate, Andrea; Barra, Silvio; Gallo, Luigi; Narducci, Fabio
Kurtosis and skewness at pixel level as input for SOM networks to iris recognition on mobile devices Journal Article
In: Pattern Recognition Letters, 2017, ISSN: 0167-8655.
Abstract | Links | BibTeX | Tags: Iris, Mobile Biometric Recognition, Statistical descriptors, Unsupervised Learning
@article{abate_kurtosis_2017,
title = {Kurtosis and skewness at pixel level as input for SOM networks to iris recognition on mobile devices},
author = {Andrea Abate and Silvio Barra and Luigi Gallo and Fabio Narducci},
url = {http://www.sciencedirect.com/science/article/pii/S0167865517300338},
doi = {10.1016/j.patrec.2017.02.002},
issn = {0167-8655},
year = {2017},
date = {2017-02-01},
urldate = {2017-02-21},
journal = {Pattern Recognition Letters},
abstract = {The increasing popularity of smartphones amongst the population laid the basis for a wide range of applications aimed at security and privacy protection. Very modern mobile devices have recently demonstrated the feasibility of using a camera sensor to access the system without typing any alphanumerical password. In this work, we present a method that implements iris recognition in the visible spectrum through unsupervised learning by means of Self Organizing Maps (SOM). The proposed method uses a SOM network to cluster iris features at pixel level. The discriminative feature map is obtained by using RGB data of the iris combined with the statistical descriptors of kurtosis and skewness. An experimental analysis on MICHE-I and UBIRISv1 datasets demonstrates the strengths and weaknesses of the algorithm, which has been specifically designed to require low processing power in compliance with the limited capability of common mobile devices.},
keywords = {Iris, Mobile Biometric Recognition, Statistical descriptors, Unsupervised Learning},
pubstate = {published},
tppubtype = {article}
}
The increasing popularity of smartphones amongst the population laid the basis for a wide range of applications aimed at security and privacy protection. Very modern mobile devices have recently demonstrated the feasibility of using a camera sensor to access the system without typing any alphanumerical password. In this work, we present a method that implements iris recognition in the visible spectrum through unsupervised learning by means of Self Organizing Maps (SOM). The proposed method uses a SOM network to cluster iris features at pixel level. The discriminative feature map is obtained by using RGB data of the iris combined with the statistical descriptors of kurtosis and skewness. An experimental analysis on MICHE-I and UBIRISv1 datasets demonstrates the strengths and weaknesses of the algorithm, which has been specifically designed to require low processing power in compliance with the limited capability of common mobile devices.
2016
Abate, Andrea; Barra, Silvio; Gallo, Luigi; Narducci, Fabio
SKIPSOM: Skewness Amp; Kurtosis of Iris Pixels in Self Organizing Maps for Iris Recognition on Mobile Devices Proceedings Article
In: 2016 23rd International Conference on Pattern Recognition (ICPR), pp. 155–159, 2016.
Abstract | Links | BibTeX | Tags: Image segmentation, Iris, Mobile handsets, Self-organizing feature maps
@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}
}
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.
Abate, Andrea; Barra, Silvio; Gallo, Luigi; Narducci, Fabio
SKIPSOM: Skewness amp; kurtosis of iris pixels in Self Organizing Maps for iris recognition on mobile devices Proceedings Article
In: 2016 23rd International Conference on Pattern Recognition (ICPR), pp. 155–159, 2016.
Abstract | Links | BibTeX | Tags: Image segmentation, Iris, Mobile handsets, Self-organizing feature maps
@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}
}
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.