IDAACS 2015

Computational Intelligence for Biometric Applications

Vincenzo Piuri University of Milan, Italy

In cooperation with Ruggero Donida Labati, Angelo Genovese, Enrique Muñoz, Fabio Scotti and Gianluca Sforza

EU FP7 Project “ABC GATES FOR EUROPE”

Summary

1. Introduction to biometrics2. Computational intelligence for biometrics3. Applications and examples

Computational intelligence for sensors Signal preprocessing Feature extraction and selection Computational intelligence for data fusion Computational intelligence for classification

and quality measurement Computational intelligence for system optimization

4. Conclusions

Biometrics

“Automated methods of recognizing a person based on physiological or behavioral characteristics”

Physiological biometrics Fingerprint, Face, Hand shape, Iris, Ear, DNA, odor, …

Behavioral biometrics Voice, Signature, Gait, Keystroke dynamics, …

Biometrics vs Classical Identification

From something you have (token, key) or something you know (password) to something you are

Identification method

Securitylevel

Something you have

Something you are

Something you know

Biometrics Systems (1)

Dimension: from embedded to AFIS (FBI)

Biometrics Systems (2)

Cooperative user or “hidden” systemCooperative

Hidden system

Biometrics Pattern Recognition

Acquisition Feature extraction

DataBase

Sample Features

Coding

Template

Acquisition

Feature Extraction

Coding

Matching

Trait

Enrollment

Identification

Yes/No

Matching Score and Biometric Threshold

DataBaseAcquisition

Feature Extraction

Coding

Matching

Identification

Yes/No

>?

Treshold = 87%

MatchingScore

Low

High

Impostor and Genuine Distributions

False Match Rate (FMR)False Non-Match Rate (FNMR)

Performance Representation

The Receiving Operating Curve (FNMR vs FMR varying the threshold t) is used to expressthe accuracy performance of the systems

The equal error rate EER (FNMR=FMR) resume the performance of the system

EER

Technologies for Biometric Systems

Sensors and measurement systems Biometric sensor, liveness tests

Signal processing Feature extraction, liveness test

Image processing Face, fingerprint, hand, iris, gait , ear

Sensor data fusion Matching module , multimodal biometric systems

Classification and clustering Organization of very-large DB of biomeric templates (National

identification systems, large scale identification systems)

Conventional Algorithmic Techniques

Computational complexity

Require a modelNot able to learn from experience

Computational Intelligence for Biometrics

Smarter

Adaptive Evolvable

Intelligent

Composite Systems for Biometrics

TRADITIONAL PARADIGMS +COMPUTATIONAL INTELLIGENCE =_________________________________+ MORE DESIGN DEGREES OF FREEDOM+ ACCURACY + PERFORMACE

Neural NetworkFuzzy

AlgorithmFilter

DesignerRoutine

Input Output

Main Problem

Tackling different aspects at the same time:

instrumentation and measurement systems

image and signal processing. feature extraction

sensor fusion

system modeling

data analysis

classification

How to Deal with Heterogeneous Aspects?

Nowadays:

Separate issues Module-oriented solutions Ad-hoc solutions

Limited optimization Limited reusability Limited integrability

A Comprehensive Design Approach

FeatureExtractio

n

Sensor Fusion

SystemModeling

DataAnalysis

Classification

Design methodology

Biometric system

Biometric system Design Methodology

A. Signal and image acquisition

B. Signal and image preprocessing

C. Feature extraction and selection

D. Data fusion

E. Classification and quality measurement

F. System optimization

A. Signal and Image Acquisition

Conventional techniques: sensor enhancement sensor linearization sensor diagnosis sensor calibration

Computational intelligence approaches self-calibration non-linearities reduction Error and faults detection

B. Signal Preprocessing

Signal preprocessing: enhancing the signals and correcting the errors

Features processing: extract from the input signals a set of features

Neural and fuzzy techniques for signal and feature processing:

Adaptivity, intelligence, learning from examples, ...

C. Feature Extraction and Selectiton

How many features?

Complexity AccuracyFew features

Many features

?!?

Curse of Dimensionality Problem

Due to an excessive number of features

d=2Space occupation= 10%

d=3Space occupation= 1%

Dimensionality reduction problem

Simplification of the classifier

Faster

Use less memory

Selection or Extraction

Feature selection:

Feature extraction:

Feature 1

Feature 2

Feature 3

Feature 4

Feature 5

Feature 6

Feature Selection

Feature 2

Feature 3

Feature 5

Feature 1

Feature 2

Feature 3

Feature 4

Feature 5

Feature 6

Feature Extraction

Feature A

Feature B

Feature C

Feature D

Selection and Extraction

Feature 1

Feature 2

Feature 3

Feature 4

Feature 5

Feature 6

Feature A

Feature B

Feature C

Feature D

Feature A

Feature C

Feature Selection

Feature Extraction

Feature Extraction Algorithms

Principal Component Analysis

Linear Discriminat Analysis Independent Component

Analysis Kernel PCA PCA network

Nonlinear PCA Feed-Forward Neural

Networks Nonlinear autoassociative

network Multidimensional Scaling Self-Organizing Map (MAP)

Feature Selection Algorithms

Exhaustive Search Branch and Bound

Sequential Forward Selection Sequential Backward Selection Sequential Floating Search methods

D. Biometric Data Fusion

MultimodalBiometrics

Multiplebiometrics

Multiplesnapshots

Multipleunits

Multiplematchers

Multiplesensors

optical andcapacitance sensors

face andfingerprint

minutiae andnon-minutiae

based matchers

two attempts or twotemplates

right index andmiddlefingers

Classical Fusion Schema

Matchscore fusion

Matchscore fusionMulti-paradigmatic

Multimodal

Features fusion

Information Fusion Levels

FM: Fusion ModuleDM: Decision ModuleMM: Matching Module

Matching Fusion Level (Results)

1.

2.

E. Computational Intelligence for Classification and Measurement

Featuresα

β

γ

...

d-dimensional vector

Classifier

an integer:classification of the quality

a floating point value:an index of quality

Classification (Quality Checker and Binning)

AcquisitionModule

Feature Extraction

Module

Template

Traits

QualityChecker

SamplesSamples

DX “arch”SX “arch”

DX “loop”SX “arch”

DX “arch”SX “loop”

DX “loop”SX “loop”

Classifier

#1

Enrollment

quality checker of input samples sub-class classification

Computational Intelligence for Classification and Measurement (2)

Computational Intelligence Techiniques

StatisticalApproaches

NeuralNerworks

FuzzyClassifiers

Ingressi

Uscite

Solve complex problems by mimicking the human reasoning

F. System Optimization

System parameters difficult to fix

Very often trial-and-error approaches

Evolutionary computation techniques can solve this optimization task

State of the Art

The conventional approach: trial and error

Design Metodology Goals

Applying the high-level system design knowledge for the semi-automatic design of biometric systems.

The choice of algorithmsto be inserted into the biometric system

The optimization of the hardware system architecture

The output produced is: ready-to-compile code suitable configuration of the hardware architecture.

What is the High-Level System Design?

High-level synthesis is the process of mapping a behavioural description at the algorithmic level to a structural description in terms of functional units, memory elements, and interconnections

The term behavioural description refers to a description of the input/output relationship of the system to be implemented.

(algorithm written, e.g., in C, C++ , VHDL, and System C)

Methodolgy (1)(2)(3)

The proposed methodology can be summarized in the three following main activities:

(1) to model the possible hardware architectures

(2) to specify the behavioural description of the biometric system for the envisioned application

(3) to map the behavioural description for the specific application into a hardware model satisfying the designer’s requirement

OPTIM A HW

figures

bio = HW(A)

Hardware Architecture Model (1)

Behavioural Description (2)

The behavioural description of the biometric system consists of the sequence of the operations that allow the biometric system to identify the person presented at its input sensors.

Mapping the Behavioural Description onto the Hardware Model (3)

The goal of the mapping phase consists of binding each component of the behavioural description, A, to the corresponding hardware resources, HW, which implement its computation in the biometric system.

The optimum mapping is an iterative process in which proper figures of merit are evaluated and in which system’s independent variables are tuned to enhance the system’s figures of merit while satisfying the design requirements.

OPTIM A HW

figures

bio = HW(A)

Figures of Merit for a Multimodal Biometric System

The most common figures of merit considered for a biometric systemcharacterize its accuracy

Indexes used: The False Match Rate (FMR) The False Non-Match Rate (FNMR) The Equal Error Rate (EER)

Error plots: Receiving Operating Curve (ROC) Detection Error Trade-off (DET)

Other figures of merit : Response time [s] Memory usage [MB] Component costs [$]

Figures and Design Requirements

Given the biometric model bio = HW(A) and the data benchData required to test the system, it is possible to evaluate the figures of merit with:

benchDataAHWfiguresfff m ,],,,[ 21

The design requirements are expressed by the designer as a set of equations in the figures of merit:

Pfffh m ),,,( 21

Example of design requirements:

MBpationmemoryOccusmeresponseTi

zeroFNMRANDzeroFMREER

42

98.002.001.0

Experimental Results

To verify the feasibility and the usability of the proposed methodology,we implemented a prototype of the methodology

EER, zeroFMR, zeroFNMR.

Matlab

Rule-based system

Conclusions

Biometric systems are critical for security Aspects in different technological areas should be

tackled at the same time A comprehensive design methodology should deal with

all aspects in an integrated way Computational intelligence offer additional

opportunities for adaptable and evolvable systems

References (1)

49

A. Genovese, V. Piuri, F. Scotti Touchless Palmprint Recognition SystemsSpringerISBN: 978-3-319-10364-8

R. Donida Labati, V. Piuri, F. Scotti Touchless Fingerprint BiometricsCRC PressISBN: 978-1-498-70761-9

A. Amato, V. Di Lecce, V. PiuriSemantic Analysis and Understanding of Human Behavior in Video Streaming SpringerISBN: 978-1-461-45485-4

References (2)

Introduction S. Z. Li, A. K. Jain, Encyclopedia of Biometrics, Springer Publishing Company,

Incorporated, 2009. M.Tistarelli, S. Z. Li, R.Chellappa, Handbook of Remote Biometrics: For Surveillance and

Securit,Springer Publishing Company, Incorporated, 2009. N. V. Boulgouris, K. N. Plataniotis, E. Micheli-Tzanakou, Biometrics: Theory, Methods, and

Applications, IEEE Computer Society Press, 2009. A. K. Jain, P. Flynn, A. Ross, Handbook of Biometrics, Springer -Verlag New York,

Incorporated, 2007.

50

References (3) Fingerprint

D. Maltoni, D. Maio, A. K. Jain, S. Prabhakar, Handbook of Fingerprint Recognition, 2nd ed., Springer Publishing Company, Incorporated, 2009.

D. Maltoni,"Fingerprint Recognition, Overview", in Encyclopedia of Biometrics. S. Z. Li and A. K. Jain, Springer Publishing Company, Incorporated, pp. 510 – 513, 2009.

R. Donida Labati, A. Genovese, V. Piuri, F. Scotti, "Toward Unconstrained Fingerprint Recognition: a Fully-Touchless 3-D System Based on Two Views on the Move", in IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2015.

V. Piuri, and F. Scotti, "Fingerprint Biometrics via Low-cost Sensors and Webcams", in Biometrics: Theory, Applications and Systems, 2008. BTAS 2008. 2nd IEEE International Conference on, pp. 1-6, October 2008.

R. Donida Labati, A. Genovese, V. Piuri, F. Scotti, "Touchless fingerprint biometrics: a survey on 2D and 3D technologies", in Journal of Internet Technology, pp. 325 - 332, May, 2014.

N. Yager, A. Amin, "Fingerprint verification based on minutiae features: a review", Pattern Analysis & Applications, Springer London, vol. 7, pp. 94-113, 2004.

P. Komarinski, Automated fingerprint identification systems (AFIS), Elsevier Academic, Amsterdam, 2005. N.K. Ratha, R.M.Bolle, Automatic Fingerprint Recognition Systems, Springer-Verlag, 2003. R. DonidaLabati, V. Piuri, and F. Scotti, "A neural-based minutiae pair identification method for

touchlessfingeprint images", in IEEE Symposium Series in Computational Intelligence 2011 (SSCI 2011), April 2011. R. Donida Labati, A. Genovese, V. Piuri, F. Scotti, "Touchless Fingerprint Biometrics: a Survey on 2D and

3D Technologies", in Journal of Internet Technology, 2014 R. Donida Labati, A. Genovese, V. Piuri, F. Scotti, "Accurate 3D Fingerprint Virtual Environment for

Biometric Technology Evaluations and Experiment Design", in Proc. of the 2013 IEEE Int. Conf. on Computational Intelligence and Virtual Environments for Measurement Systems and Applications (CIVEMSA 2013), Milan, Italy, pp. 43 - 48, July 15 - 17, 2013

51

References (4)

Fingerprint (cont’d) R. Donida Labati, A. Genovese, V. Piuri, F. Scotti, "Contactless Fingerprint Recognition: a Neural

Approach for Perspective and Rotation Effects Reduction", in Proc. of the IEEE Workshop on Computational Intelligence in Biometrics and Identity Management (CIBIM), Singapore, Singapore, pp. 22 - 30, April 16 - 19, 2013

R. DonidaLabati, V. Piuri, F. Scotti, "Measurement of the principal singular point in fingerprint images: a neural approach", in 2010 IEEE International Conference on Computational Intelligence for Measurement Systems and Applications (CIMSA), pp. 18 - 23, September 6-8, 2010.

R. Donida Labati, V. Piuri, F. Scotti, "Neural-based Quality Measurement of Fingerprint Images in Contactless Biometric Systems", in The 2010 International Joint Conference on Neural Networks (IJCNN), pp. 1 – 8, July 18-23, 2010.

M . Gamassi, V. Piuri, and F. Scotti, "Fingerprint local analysis for high-performance minutiae extraction", in IEEE International Conference on Image Processing, 2005 (ICIP 2005), pp. III - 265-8, September, 2005

R. Donida Labati, A. Genovese, V. Piuri, and F. Scotti, "Two-view Contactless Fingerprint Acquisition Systems: a Case Study for Clay Artworks", in 2012 IEEE Workshop on Biometric Measurements and Systems for Security and Medical Applications, 2012

R. Donida Labati, A. Genovese, V. Piuri, and F. Scotti, "Virtual Environment for 3-D Synthetic Fingerprints", 2012 IEEE International Conference on Virtual Environments, Human-Computer Interfaces and Measurement Systems, pp. 48 - 53, 2012

R. Donida Labati, A. Genovese, V. Piuri, and F. Scotti, "Quality Measurement of Unwrapped Three-dimensional Fingerprints: a Neural Networks Approach", in 2012 International Joint Conference on Neural Networks (IJCNN 2012), pp. 1123 - 1130, 2012

R. Donida Labati, A. Genovese, V. Piuri, and F. Scotti, "Fast 3-D Fingertip Reconstruction Using a Single Two-View Structured Light Acquisition", in IEEE Workshop on Biometric Measurements and Systems for Security and Medical Applications, pp. 1 - 8, 2011

52

References (5)

Fingerprint (cont’d) R. Donida Labati, V. Piuri, and F. Scotti, "A neural-based minutiae pair identification method for

touchless fingeprint images", in 2011 IEEE Workshop on Computational Intelligence in Biometrics and Identity Management (CIBIM), pp. 96 -102, April, 2011

R. Donida Labati, V. Piuri, and F. Scotti, "Neural-based Quality Measurement of Fingerprint Images in Contactless Biometric Systems", in The 2010 International Joint Conference on Neural Networks (IJCNN), pp. 1 - 8, July 18-23, 2010

R. Donida Labati, A. Genovese, V. Piuri, and F. Scotti, "Measurement of the Principal Singular Point in Contact and Contactless Fingerprint Images by using Computational Intelligence Techniques", in 2010 IEEE International Conference on Computational Intelligence for Measurement Systems and Applications (CIMSA 2010), pp. 18 - 23, 2010

53

References (6)

Iris R. Donida Labati, A. Genovese, V. Piuri, and F. Scotti, "Iris segmentation: state of the art and innovative

methods", in Cross Disciplinary Biometric Systems, C. Liu, and V.K. Mago (eds.), Springer, pp. 151-182, 2012 H. Proença, "Quality Assessment of Degraded Iris Images Acquired in the Visible Wavelength", IEEE

Transactions on Information Forensics and Security,vol.6, no.1, pp.82-95, March 2011. Yung-hui Li, M. Savvides,"Iris Recognition, Overview", in Encyclopedia of Biometrics.S. Z. Li and A. K. Jain,

Springer Publishing Company, Incorporated, pp. 810 – 819, 2009. K.W. Bowyer, K. Hollingsworth and P.J. Flynn, Image understanding for iris biometrics: a survey,

Computer Vision and Image Understanding, vol. 110, pp. 281-307, 2008. J. Daugman, "New Methods in Iris Recognition", IEEE Transactions on Systems, Man, and Cybernetics, Part B:

Cybernetics, vol.37, no.5, pp.1167-1175, October 2007. V. Piuri, and F. Scotti, "Adaptive Reflection Detection and Location in Iris Biometric Images by Using

Computational Intelligence Techniques", in IEEE Transactions of Instrumentation and Measurement, pp.1825-1833, July 2010.

R. Donida Labati, and F. Scotti, "Noisy iris segmentation with boundary regularization and reflections removal", in Image and Vision Computing, Iris Images Segmentation Special Issue, Elsevier, pp. 270-277, February 2010.

R. Donida Labati, V. Piuri, and F. Scotti, "Neural-based Iterative Approach for Iris Detection in Iris recognition systems", in IEEE Symposium on Computational Intelligence for Security and Defence Applications, pp. 1-6, December 18, 2009.

R. Donida Labati, V. Piuri, and F. Scotti, "Agent-Based Image Iris Segmentation and Multiple Views Boundary Refining", in IEEE Third International Conference on Biometrics: Theory, Applications and Systems, pp. 1-7, November 20, 2009.

54

References (7)

Face Yun Fu, GuodongGuo, T. S. Huang, "Age Synthesis and Estimation via Faces: A Survey",IEEE

Transactions on Pattern Analysis and Machine Intelligence, vol.32, no.11, pp.1955-1976, November 2010. A. M. Martinez, "Face Recognition, Overview", in Encyclopedia of Biometrics. S. Z. Li and A. K. Jain,

Springer Publishing Company, Incorporated, pp. 555 – 559, 2009. S. Romdhani,J. Ho, T. Vetter, D. J.Kriegman, "Face Recognition Using 3-D Models: Pose and

Illumination",Proceedings of the IEEE , vol.94, no.11, pp.1977-1999, November 2006. Z. Li, A. K. Jain, Handbook of Face Recognition, Springer-Verlag, 2005. W. Zhao, R. Chellappa, A. Rosenfeld, P.J. Phillips, "Face Recognition: A Literature Survey", ACM

Computing Surveys, pp. 399-458S, 2003. S. S. Rakover& B. Cahlon,Face recognition: cognitive and computational processes, John Benjamins Publishing Co.,

Amsterdam, The Netherlands, 2001. Ear shape

M.Choras, "Ear Biometrics", in Encyclopedia of Biometrics. S. Z. Li and A. K. Jain, Springer Publishing Company, Incorporated, pp. 233 – 240, 2009.

B.Bhanu, H. Chen, Human Ear Recognition by Computer (Advances in Pattern Recognition), Springer Publishing Company, Incorporated, 2008.

D. J. Hurley, B. Arbab-Zavar, M. S. Nixon, “The Ear as a Biometric”, in: Handbook of Biometrics, pp. 131-150. A. K. Jain, P. Flynn, A. Ross,Springer -Verlag New York, Incorporated, 2007.

S. M. S. Islam, M.Bennamoun, R. A. Owens, R. Davies, "Biometric Approaches of 2D-3D Ear and Face: A Survey", in Advances in Computer and Information Sciences and Engineering. Springer Netherlands, pp. 509-514 , 2007.

55

References (8)

Hand geometry N. Duta, "A survey of biometric technology based on hand shape", Pattern Recognition, vol. 42, n. 11,

pp. 2797-2806, November 2009. N. Duta, "Hand Shape", in Encyclopedia of Biometrics. S. Z. Li and A. K. Jain, Springer Publishing Company,

Incorporated, pp. 682 – 687, 2009. R. Sanchez-Reillo, C. Sanchez-Avila, A. Gonzalez-Marcos, "Biometric identification through hand

geometry measurements," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.22, no.10, pp. 1168- 1171, October 2000.

Palmprint & Palmvein D. Zhang, Z.Guo, G. Lu, L. Zhang, Y. Liu, W.Zuo, "Online joint palmprint and palmvein verification",

Expert Systems with Applications, vol. 38, no. 3, pp. 2621-2631, March 2011. A. Kong, D. Zhang, M. Kamel, "A Survey of Palmprint Recognition", Pattern Recognition, vol. 42, no. 7,

pp. 1408-1418, July 2009. M. Watanabe, " Palm Vein", in Encyclopedia of Biometrics. S. Z. Li and A. K. Jain, Springer Publishing

Company, Incorporated, pp. 1028 – 1033, 2009. D. Zhang, V. Kanhangad, " Palmprint, 3D", in Encyclopedia of Biometrics. S. Z. Li and A. K. Jain, Springer

Publishing Company, Incorporated, pp. 1037 – 1042, 2009. ECG

R. Donida Labati, V. Piuri, R. Sassi, G. Sforza, F. Scotti, "Adaptive ECG biometric recognition: a study on re-enrollment methods for QRS signals", in Proc. of the IEEE Workshop on Computational Intelligence in Biometrics and Identity Management (CIBIM 2014), Orlando, FL, USA, pp. 30 - 37, December 9-12, 2014.

R. Donida Labati, V. Piuri, R. Sassi and F. Scotti, "HeartCode: a novel binary ECG-based template", in Proc. of the IEEE Workshop on Biometric Measurements and Systems for Security and Medical Applications (BioMS2014), Rome, Italy, October 17, 2014.

56

References (9)

DNA J.M. Butler, Fundamentals of Forensic DNA Typing, Elsevier Academic Press, San Diego, 2010. R. AH van Oorschot, K. N. Ballantyne, R. J. Mitchell, "Forensic trace DNA: a review", Investigative

Genetics, pp. 1 – 14, 2010. T. Hicks, R. Coquoz, " Forensic DNA Evidence", in Encyclopedia of Biometrics. S. Z. Li and A. K. Jain,

Springer Publishing Company, Incorporated, pp. 573 – 579, 2009. P. M. Vallone, C. R. Hill, J. M. Butler, "Demonstration of rapid multiplex PCR amplification involving 16

genetic loci", Forensic Science International: Genetics, vol. 3, no. 1, pp. 42-45, December 2008.

57

References (10)

Voice H. Beigi, Fundamentals of Speaker Recognition, Springer-Verlag New York Inc., January 2011. J. Markowitz, "Speaker Recognition, Standardization", in Encyclopedia of Biometrics. S. Z. Li and A. K. Jain,

Springer Publishing Company, Incorporated, pp. 1270 – 1277, 2009. J. Benesty, M. MohanSondhi, Y Huang, Springer Handbook of Speech Processing, Springer-Verlag, January

2008. R. D. Peacocke, D. H. Graf, "An introduction to speech and speaker recognition",Computer , vol.23, no.8,

pp.26-33, August 1990. Gait

M. Goffredo, I.Bouchrika, J. N. Carter, M. S. Nixon, "Self-Calibrating View-Invariant Gait Biometrics",IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, vol.40, no.4, pp.997-1008, August 2010.

R.Chellappa, A.Veeraraghavan, N.Ramanathan, "Gait Biometrics, Overview", in Encyclopedia of Biometrics. S. Z. Li and A. K. Jain, Springer Publishing Company, Incorporated, pp. 628 – 633, 2009.

M.S.Nixon, J. N. Carter, "Automatic Recognition by Gait," Proceedings of the IEEE , vol.94, no.11, pp.2013-2024, November 2006.

N.V. Boulgouris, D. Hatzinakos, K.N. Plataniotis, "Gait recognition: a challenging signal processing technology for biometric identification",IEEE Signal Processing Magazine, vol.22, no.6, pp. 78- 90, November 2005.

58

References (11)

Signature & hand writing V. A. Bharadi, H. B. Kekre, "Off-Line Signature Recognition Systems", International Journal of Computer

Applications vol. 1, no. 27, pp. 48–56, February 2010. O. Henniger, D. Muramatsu, T. Matsumoto, I. Yoshimura, M. Yoshimura, " Signature Recognition", in

Encyclopedia of Biometrics. S. Z. Li and A. K. Jain, Springer Publishing Company, Incorporated, pp. 1196 –1205, 2009.

D. Impedovo, G. Pirlo, "Automatic Signature Verification: The State of the Art", IEEE Transactions onSystems, Man, and Cybernetics, Part C: Applications and Reviews, vol.38, no.5, pp.609-635, September 2008.

Keystroke N.Bartlow, "Keystroke Recognition", in Encyclopedia of Biometrics. S. Z. Li and A. K. Jain, Springer

Publishing Company, Incorporated, pp. 877 – 882, 2009. D. Shanmugapriya, "A survey of biometric keystroke dynamics: approaches, security and challenges",

International Journal of Computer Science and Information Security, vol. 5, pp. 115 – 119, September 2009. Enzhe Yu, Sungzoon Cho, "Keystroke dynamics identity verification - its problems and practical

solutions", Computers & Security, vol. 23, no. 5, pp. 428-440, July 2004. Weight

R. Donida Labati, A. Genovese, V. Piuri, and F. Scotti, "Weight Estimation from Frame Sequences Using Computational Intelligence Techniques", 2012 IEEE International Conference on Computational Intelligence for Measurement Systems and Applications (CIMSA 2012), pp. 29 - 34, 2012

59

References (12)

Biometric Privacy M. Upmanyu, A. Namboodiri, K. Srinathan, and C. Jawahar, "Blind authentication: A secure crypto-

biometric verification protocol", Information Forensics and Security, IEEE Transactions on, vol. 5, no. 2, pp. 255 –268, June2010.

J. Golic, M. Baltatu, “Entropy analysis and new constructions of biometric key generation systems,”IEEE Transactions on Information Theory,vol. 54, no. 5,pp. 2026–2040, 2008.

A. K. Jain, K. Nandakumar, A. Nagar, "Biometric template security",EURASIP Journal on Advances Signal Processing, vol. 2008, pp. 1-17, 2008.

Y. Dodis, R. Ostrovsky, L. Reyzin, and A. Smith, "Fuzzy extractors: How to generate strong keys from biometrics and other noisy data", SIAM Journal on Computing, vol. 38, no. 1, pp. 97–139, 2008.

N. K. Ratha, S. Chikkerur, J. H. Connell, and R. M. Bolle, "Generating cancelable fingerprint templates", IEEE Transaction on Pattern Analysis and Machine Intelligence, vol. 29, no. 4, pp. 561–572, 2007.

A. Teoh, A. Goh, and D. Ngo, "Randommultispace quantization as an analyticmechanismforbiohashingof biometric and random identity inputs", IEEE Transactions onPattern Analysis and Machine Intelligence, vol. 28, no. 12, pp. 1892 –1901, December 2006.

M. Barni, T. Bianchi, D. Catalano, M. Di Raimondo, R. DonidaLabati, P. Failla, D. Fiore, R. Lazzeretti, V. Piuri, F. Scotti, and A. Piva, "A Privacy-compliant Fingerprint Recognition System Based on Homomorphic Encryption and Fingercode Templates", in 2010 Fourth IEEE International Conference on Biometrics: Theory Applications and Systems (BTAS), pp. 1-7, September 27-29, 2010.

60

References (13)

Biometric Privacy (cont’s) M. Barni, T. Bianchi, D. Catalano, M. Di Raimondo, R. DonidaLabati, P. Failla, D. Fiore, R. Lazzeretti,

V. Piuri, F. Scotti, and A. Piva, "Privacy-Preserving Fingercode Authentication", in Proceedings of the 12th ACM workshop on Multimedia and security, ACM, New York, NY, USA, pp. 231 - 240, September 9-10, 2010.

T. Bianchi, R. Donida Labati, V. Piuri, A. Piva, F. Scotti, S. Turchi, "Implementing FingerCode-Based Identity Matching in the Encrypted Domain", in 2010 IEEE Workshop on Biometric Measurements and Systems for Security and Medical Applications (BIOMS), pp. 15 - 21, September 9, 2010.

S. Cimato, M. Gamassi, V. Piuri, R. Sassi, and F. Scotti, "Privacy in Biometrics", in Biometrics: Theory, Methods, and Applications, Wiley-IEEE Press, 2008.

S. Cimato, M. Gamassi, V. Piuri, R. Sassi, and F. Scotti, "Privacy-Aware Biometrics: Design and Implementation of a Multimodal Verification System", in Annual Computer Security Applications Conference, 2008. ACSAC 2008, pp. 130-139, December, 2008

S. Cimato, M. Gamassi, V. Piuri, R. Sassi, and F. Scotti, "A Multi-Biometric Verification System for the Privacy Protection of Iris Templates", in International Workshop on Computational Intelligence in Security for Information Systems, October 23-24, 2008

S. Cimato, M. Gamassi, V. Piuri, R. Sassi, F. Cimato, and F. Scotti, "A Biometric Verification System Addressing Privacy Concerns", in Computational Intelligence and Security, 2007 International Conference on, pp. 594-598, December 2007.

S. Cimato, M. Gamassi, V. Piuri, D. Sana, R. Sassi, and F. Scotti, "Personal identification and verification using multimodal biometric data", in Proceedings of the 2006 IEEE International Conference on Computational Intelligence for Homeland Security and Personal Safety, pp. 41-45, October, 2006

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