- Marco Querini is a research associate at the department of Civil Engineering and Computer Science Engineering, Univer... moreMarco Querini is a research associate at the department of Civil Engineering and Computer Science Engineering, University of Rome ”Tor Vergata”. He received the Master’s degree in Computer Engineering from the University of Rome ”Tor Vergata” in 2010.
From the same university, he received the PhD in Computer Science and Automation Engineering in 2014, defending a thesis which presented a new system for securizing documents through their entire life cycle. His research focuses on computer security, high capacity barcodes in security applications, document securization and verification, handwritten signature verification. He is author of a number of publications in those fields, which also address challenges given by todays exploding world of mobile devices. Eng.
PhD. Marco Querini is also serving as program committee member of IEEE conference such as MMAP 2014, MMAP 2015.edit
Research Interests:
ABSTRACT 2D color barcodes have been introduced to obtain larger storage capabilities than traditional black and white barcodes. Unfortunately, the data density of color barcodes is substantially limited by the redundancy needed for... more
ABSTRACT 2D color barcodes have been introduced to obtain larger storage capabilities than traditional black and white barcodes. Unfortunately, the data density of color barcodes is substantially limited by the redundancy needed for correcting errors, which are due not only to geometric but also to chromatic distortions introduced by the printing and scanning process. The higher the expected error rate, the more redundancy is needed for avoiding failures in barcode reading, and thus, the lower the actual data density. Our work addresses this trade-off between reliability and data density in 2D color barcodes and aims at identifying the most effective algorithms, in terms of byte error rate and computational overhead, for decoding 2D color barcodes. In particular, we perform a thorough experimental study to identify the most suitable color classifiers for converting analog barcode cells to digital bit streams.
Research Interests:
Handwritten Signature Verification (HSV) is a natural and trusted method for user identity verification. HSV can be classified into two main categories: offline and online HSV. Offline systems take handwritten signatures from scanned... more
Handwritten Signature Verification (HSV) is a natural and trusted method for user identity verification. HSV can be classified into two main categories: offline and online HSV. Offline systems take handwritten signatures from scanned documents, while online systems use specific hardware (e.g., pen tablets) to register pen movements during the act of signing. Online HSV systems may embed signatures (including the signature dynamics) into digital documents. Unfortunately, during their lifetime documents may be repeatedly printed and scanned, and digital to paper conversions may result in loosing the signature dynamics. The main contribution of this work is a new HSV system for secure handwritten signing of documents. First, we illustrate how to verify handwritten signatures so that signature dynamics can be processed during verification of every type of document (both paper and digital documents). Secondly, we show how to embed features extracted from handwritten signatures within the...
Research Interests:
This paper proposes a novel use of 2D barcodes to store biometric data, in particular for facial recognition, which represents one of the least invasive biometric techniques. To accomplish this, we deploy 2D color barcodes, which allow... more
This paper proposes a novel use of 2D barcodes to store biometric data, in particular for facial recognition, which represents one of the least invasive biometric techniques. To accomplish this, we deploy 2D color barcodes, which allow larger storage capabilities than traditional 2D barcodes. To improve the quality of facial recognition, we combine local feature descriptors, such as SURF descriptors, together with shape landmarks identified through statistical models for discriminating faces. The biometric information can be secured through digital signature algorithms, in order to protect biometric data from malicious tampering. The use of color barcodes is crucial in this setting, as traditional barcodes cannot store a suitable number of SURF descriptors for discriminating faces and cannot even afford to store an additional cryptographic payload. We report the results of an experimental evaluation of our system on real-world data sets (i.e., a face database).
Handwritten Signature Verification (HSV) is a natural and trusted method for user identity verification. HSV can be classified into two main categories: offline and online HSV. Offline systems take handwritten signatures from scanned... more
Handwritten Signature Verification (HSV) is a natural and trusted method for user identity verification. HSV can be classified into two main categories: offline and online HSV. Offline systems take handwritten signatures from scanned documents, while online systems use specific hardware (e.g., pen tablets) to register pen movements during the act of signing. Online HSV systems may embed signatures (including the signature dynamics) into digital documents. Unfortunately, during their lifetime documents may be repeatedly printed and scanned, and digital to paper conversions may result in loosing
the signature dynamics. The main contribution of this work is a new HSV system for secure handwritten signing of documents. First, we illustrate how to verify handwritten signatures so that signature dynamics can be processed during verification of every type of document (both paper and digital documents). Secondly, we show how to embed features
extracted from handwritten signatures within the documents themselves, so that no remote signature database is needed. To accomplish the embedding task, we make use
of 2D barcodes. The main challenge here is to be able to store the signature dynamics within the limited capacity of barcodes. Thirdly, we propose a method for the verification of signature dynamics which is compatible to a wide range of mobile devices so that no special hardware is needed. The main challenge here is to achieve a high verification performance, despite constrains due to the limited computational resources and pressure
accuracy of mobile phones. We address the trade-off between discrimination capabilities of the system and the storage size of the signature model. Towards this end, we report the results of an experimental evaluation of our system on different signature datasets.
the signature dynamics. The main contribution of this work is a new HSV system for secure handwritten signing of documents. First, we illustrate how to verify handwritten signatures so that signature dynamics can be processed during verification of every type of document (both paper and digital documents). Secondly, we show how to embed features
extracted from handwritten signatures within the documents themselves, so that no remote signature database is needed. To accomplish the embedding task, we make use
of 2D barcodes. The main challenge here is to be able to store the signature dynamics within the limited capacity of barcodes. Thirdly, we propose a method for the verification of signature dynamics which is compatible to a wide range of mobile devices so that no special hardware is needed. The main challenge here is to achieve a high verification performance, despite constrains due to the limited computational resources and pressure
accuracy of mobile phones. We address the trade-off between discrimination capabilities of the system and the storage size of the signature model. Towards this end, we report the results of an experimental evaluation of our system on different signature datasets.
Research Interests:
We introduce a novel use of 2D barcodes for storing facial biometrics. To accomplish this, we design 2D color barcodes with larger storage capabilities than traditional 2D barcodes. Biometric data are secured through digital signature so... more
We introduce a novel use of 2D barcodes for storing facial biometrics. To accomplish this, we design 2D color barcodes with larger storage capabilities than traditional 2D barcodes. Biometric data are secured through digital signature so as to be protected from malicious tampering. In order to improve the quality of facial recognition, we combine eigenfaces, shape landmarks identified through statistical models and local features such as SURF descriptors. The use of color barcodes is crucial in this setting, as traditional barcodes cannot store all data required for discriminating faces (e.g., a suitable number of SURF descriptors). We report the results of an experimental evaluation of our system on real-world datasets (i.e., a face database). In spite of the reduced storage available on 2D barcodes, we do not observe any degradation in precision and accuracy
Research Interests:
2D color barcodes have been introduced to obtain larger storage capabilities than traditional black and white barcodes. Unfortunately, the data density of color barcodes is substantially limited by the redundancy needed for correcting... more
2D color barcodes have been introduced to obtain
larger storage capabilities than traditional black and white
barcodes. Unfortunately, the data density of color barcodes is
substantially limited by the redundancy needed for correcting
errors, which are due not only to geometric but also to chromatic distortions introduced by the printing and scanning process. The higher the expected error rate, the more redundancy is needed for avoiding failures in barcode reading, and thus, the lower the actual data density. Our work addresses this trade-off between reliability and data density in 2D color barcodes and aims at identifying the most effective algorithms, in terms of byte error rate and computational overhead, for decoding 2D color barcodes. In particular, we perform a thorough experimental study to identify the most suitable color classifiers for converting analog barcode cells to digital bit streams.
larger storage capabilities than traditional black and white
barcodes. Unfortunately, the data density of color barcodes is
substantially limited by the redundancy needed for correcting
errors, which are due not only to geometric but also to chromatic distortions introduced by the printing and scanning process. The higher the expected error rate, the more redundancy is needed for avoiding failures in barcode reading, and thus, the lower the actual data density. Our work addresses this trade-off between reliability and data density in 2D color barcodes and aims at identifying the most effective algorithms, in terms of byte error rate and computational overhead, for decoding 2D color barcodes. In particular, we perform a thorough experimental study to identify the most suitable color classifiers for converting analog barcode cells to digital bit streams.