Computational Intelligence
for Biometric Applications
Vincenzo Piuri
Università degli Studi di Milano, Italy
In cooperation with Ruggero Donida Labati, Angelo Genovese,
Enrique Muñoz, Fabio Scotti and Gianluca Sforza
EU FP7 Project “ABC GATES
FOR EUROPE”
IDAACS 2015
Summary
1.
2.
3.
Introduction to biometrics
Computational intelligence for biometrics
Applications and examples
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4.
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
Conclusions
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Biometrics
“Automated methods of recognizing a person
based on physiological or behavioral characteristics”
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Physiological biometrics
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Fingerprint, Face, Hand shape, Iris, Ear, DNA, Odor, …
Behavioral biometrics
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Voice, Signature, Gait, Keystroke dynamics, …
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Biometrics vs
Classical Identification

From something you have (token, key) or something
you know (password) to something you are
Security
level
Something you are
Something you know
Something
you have
Identification method
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Biometrics Systems (1)

Dimension: from embedded to AFIS (FBI)
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Biometrics Systems (2)
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Cooperative user or “hidden” system
Cooperative
Hidden system
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Biometrics  Pattern Recognition
Trait
Sample
Features
Feature
extraction
Acquisition
Coding
Template
Enrollment
Database
Identification
Acquisition
Feature
Extraction
Coding
© 2015 Vincenzo Piuri
Matching
Yes/No
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Matching Score and Biometric Threshold
Identification
Database
Acquisition
Feature
Extraction
Matching
Coding
High
Treshold = 87%
Matching
Score
>?
Low
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Yes/No
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Impostor and Genuine Distributions
False Match Rate (FMR)
False Non-Match Rate (FNMR)
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Performance Representation


The Receiving Operating
Curve (FNMR vs FMR
varying the threshold t)
is used to express
the accuracy performance
of the systems
The equal error rate EER
(FNMR=FMR)
resume the performance
of the system
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EER
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Technologies for Biometric Systems

Sensors and measurement systems
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Signal processing
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Face, fingerprint, hand, iris, gait , ear
Sensor data fusion

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Feature extraction, liveness test
Image processing

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Biometric sensor, liveness tests
Matching module , multimodal biometric systems
Classification and clustering

Organization of very-large DB of biomeric templates (National
identification systems, large scale identification systems)
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Conventional Algorithmic Techniques
Computational complexity
Require a model
Not able to learn from experience
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Computational Intelligence
for Biometrics
Intelligent
Smarter
Adaptive
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Evolvable
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Composite Systems for Biometrics
Input
Neural Network
Filter
Fuzzy
Algorithm
Output
Designer
Routine
TRADITIONAL PARADIGMS +
COMPUTATIONAL INTELLIGENCE =
_________________________________
+ MORE DESIGN DEGREES OF FREEDOM
+ ACCURACY
+ PERFORMACE
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Main Problem
Tackling different aspects at the same time:
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Instrumentation and measurement systems
Image and signal processing.
Feature extraction
Sensor fusion
System modeling
Data analysis
Classification
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How to Deal
with Heterogeneous Aspects?
Nowadays:
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Separate issues
Module-oriented solutions
Ad-hoc solutions
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© 2015 Vincenzo Piuri
Limited optimization
Limited reusability
Limited integrability
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A Comprehensive Design Approach
Feature
Extraction
System
Modeling
Sensor
Fusion
Data
Analysis
Classification
Design methodology
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Biometric
system
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Biometric system
Design Methodology
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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
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A. Signal and Image Acquisition

Conventional techniques:
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Sensor enhancement
Sensor linearization
Sensor diagnosis
Sensor calibration
Computational intelligence approaches

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Self-calibration
Non-linearity reduction
Error and faults detection
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B. Signal Preprocessing

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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:

© 2015 Vincenzo Piuri
Adaptivity, intelligence, learning from examples, ...
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C. Feature Extraction and Selectiton

How many features?
Complexity
Accuracy
Few features


Many features


?!?
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Curse of Dimensionality Problem

Due to an excessive number of features
d=2
d=3
Space occupation= 10%
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Space occupation= 1%
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Dimensionality reduction problem
Simplification of the classifier
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Faster
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Use less memory
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Selection or Extraction

Feature
selection:
Feature 1
Feature 2
Feature 3
Feature
Selection
Feature 4
Feature 2
Feature 3
Feature 5
Feature 5
Feature 6

Feature
extraction:
Feature 1
Feature 2
Feature 3
Feature A
Feature
Extraction
Feature B
Feature 4
Feature C
Feature 5
Feature D
Feature 6
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Selection and Extraction
Feature 1
Feature 2
Feature 3
Feature 4
Feature 5
Feature A
Feature
Extraction
Feature B
Feature C
Feature
Selection
Feature A
Feature C
Feature D
Feature 6
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Feature Extraction Algorithms





Principal Component
Analysis
Linear Discriminant Analysis
Independent Component
Analysis
Kernel PCA
PCA network
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




Nonlinear PCA
Feed-Forward Neural
Networks
Nonlinear autoassociative
network
Multidimensional Scaling
Self-Organizing Map (MAP)
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Feature Selection Algorithms





Exhaustive Search
Branch and Bound
Sequential Forward Selection
Sequential Backward Selection
Sequential Floating Search methods
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D. Biometric Data Fusion
optical and
capacitance sensors
Multiple
sensors
Multiple
biometrics
face and
fingerprint
© 2015 Vincenzo Piuri
Multiple
matchers
Multimodal
Biometrics
minutiae and
non-minutiae
based matchers
Multiple
snapshots
Multiple
units
two attempts or two
templates
right index and
middlefingers
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Classical Fusion Schema
Multimodal
Match score fusion
Features fusion
DB1
DB1
Feature
Extraction 1
Feature
Extraction 2
Feature
Extraction 1
score
Fusion
Matching
Decision
Feature
Extraction 2
Matching 1
score
Fusion
Matching 2
Decision
yes/no
yes/no
DB2
Multi-paradigmatic
Feature
Extraction 1
Feature
Extraction 2
DB1
Matching 1
Match score fusion
score
Fusion
Matching 2
Decision
yes/no
DB2
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Information Fusion Levels
FM: Fusion Module
DM: Decision Module
MM: Matching Module
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Matching Fusion Level (Results)
DB1
Feature
Extraction 1
Feature
Extraction 2
Matching 1
score
Fusion
Matching 2
Decision
yes/no
DB2
1.
2.
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E. Computational Intelligence
for Classification and Measurement
Features
an integer:
α
...
© 2015 Vincenzo Piuri
Classifier
γ
d-dimensional vector
β
classification
of the quality
a floating point value:
an index of quality
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Classification (Quality Checker and Binning)
Enrollment
Acquisition
Module
Quality
Checker
Feature
Extraction
Module
Classifier
Traits
#1
Samples
Samples
Template
 Quality checker of input samples
 Sub-class classification
DX “arch”
SX “arch”
© 2015 Vincenzo Piuri
DX “loop”
SX “arch”
DX “arch”
SX “loop”
DX “loop”
SX “loop”
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Computational Intelligence
for Classification and Measurement (2)
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Computational Intelligence Techiniques
Statistical
Approaches
Neural
Networks
Fuzzy
Classifiers
Uscite
Ingressi
Solve complex problems by mimicking the human reasoning
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F. System Optimization

System parameters difficult to fix

Very often trial-and-error approaches

Evolutionary computation techniques can solve this
optimization task
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State of the Art

The conventional approach: trial and error
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Design Methodology Goals

Applying the high-level system design knowledge
for the semi-automatic design of biometric systems.

The choice of algorithms
to 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.
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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)
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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
bio = HW ( A)
OPTIM
A
HW
figures
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Hardware Architecture Model (1)
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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.
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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.
bio = HW ( A)
OPTIM
A
HW
figures
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Figures of Merit for a
Multimodal Biometric System

The most common figures of merit considered for a biometric system
characterize its accuracy

Indexes used:

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Error plots:

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The False Match Rate (FMR)
The False Non-Match Rate (FNMR)
The Equal Error Rate (EER)
Receiving Operating Curve (ROC)
Detection Error Trade-off (DET)
Other figures of merit :



Response time
Memory usage
Component costs
© 2015 Vincenzo Piuri
[s]
[MB]
[$]
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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:
[ f1, f 2 ,, f m ]  figures HW  A, benchData 

The design requirements are expressed by the designer as a set of equations in the figures of
merit:
h( f1, f 2 , , f m )  P

Example of design requirements:
© 2015 Vincenzo Piuri
EER  0.01

 zeroFMR  0.02 AND zeroFNMR  0.98


responseTi me  2s


memoryOccupation  4MB

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Experimental Results

To verify the feasibility and the usability of the proposed methodology,
we implemented a prototype of the methodology
Matlab
EER, zeroFMR, zeroFNMR.
Rule-based system
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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
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References (1)
R. Donida Labati, V. Piuri, F. Scotti
Touchless Fingerprint Biometrics
CRC Press
ISBN: 978-1-498-70761-9
A. Genovese, V. Piuri, F. Scotti
Touchless Palmprint Recognition Systems
Springer
ISBN: 978-3-319-10364-8
A. Amato, V. Di Lecce, V. Piuri
Semantic Analysis and Understanding
of Human Behavior in Video Streaming
Springer
ISBN: 978-1-461-45485-4
© 2015 Vincenzo Piuri
References (2)
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Introduction
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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.
© 2015 Vincenzo Piuri
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Fingerprint
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References (3)
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 FullyTouchless 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. Donida Labati, V. Piuri, and F. Scotti, "A neural-based minutiae pair identification method for touchless
fingerprint 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
© 2015 Vincenzo Piuri
References (4)
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Fingerprint (cont’d)
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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. Donida Labati, 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 Threedimensional 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
© 2015 Vincenzo Piuri
References (5)
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Fingerprint (cont’d)
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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
© 2015 Vincenzo Piuri
References (6)
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Iris
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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.18251833, 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.
© 2015 Vincenzo Piuri
References (7)
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Face
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Yun Fu, Guodong Guo, 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
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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. 131150. 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. 509514 , 2007.
© 2015 Vincenzo Piuri
References (8)
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Hand geometry
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Palmprint & Palmvein
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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.
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
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