1

Biometric Synthesis

Dr. Marina Gavrilova

2

Topics

Biometric synthesis Image based Statistics based Examples for fingerprint, face,

signature and iris synthesis Conclusions

3

Introduction

Contemporary techniques and achievements in biometrics are beingdeveloped in two directions:

Analysis for identification and recognition of humans (direct problems)andSynthesis of biometric information (inverse problems)

4

Basic tools for inverse biometric problems include facilities for generation of synthetic data and its analysis

Introduction

5

Analysis-by-synthesis approach in facial image

Introduction

6

Synthesis approaches

There are two approaches to synthetic biometric data design:

(a) Image synthesis-based, and

(b) Statistical physics-based.

Both approaches use statistical models in the form of equations

based on underlying physics or empirically derived algorithms,

which use pseudorandom numbers to create data that arestatistically equivalent to real data. For example, in face

modeling,a number of ethnic or race models can be used to represent

ethnicdiversity, the specific ages and genders of individuals, and

otherparameters for simulating a variety of tests.

7

Image synthesisThe image synthesis-based approach falls into the area of computer graphics, a very-well explored area with application from forensics (face reconstruction) to computeranimation.A taxonomy for the creation of physics-based and empirically derived models for thecreation of statistical distributions of synthetic biometrics was first attempted in [4].There are several factors affected the modeling biometric data: behavior, sensor,and environmental factors.

Behavior, or appearance, factors are best understood as anindividuals presentation of biometric information. For example, a facialimage can be camouflaged with glasses, beards, wigs, make-up, etc.

Sensor factors include resolution, noise, and sensor age, and can beexpressed using physics-based or geometry-based equations. This factor isalso relevant to the skills of the user of the system.

Environmental factors affect the quality of collected data. Forexample, light, smoke, fog, rain or snow can affect the acquisition of visual bandimages, degrading the biometric facial recognition algorithm. High humidity ortemperature can affect infrared images. This environmental influence affects theacquisition of fingerprint images differently for different types of fingerprint sensors.

8

Synthetic fingerprints

Albert Wehde was the first to “forge" fingerprints in the 1920's. Wehde“designed" and manipulated the topology of synthetic fingerprints at

thephysical level. The forgeries were of such high quality that professionalscould not recognize them. Today's interest in automaticfingerprint synthesis addresses the urgent problems of testing

fingerprintidentification systems, training security personnel, biometric databasesecurity, and protecting intellectual property.Traditionally, two possibilities of fingerprint imitation are discussed withrespect to obtaining unauthorized access to a system: (i) the authorizeduser provides his fingerprint for making a copy, and (ii) a fingerprint istaken without the authorized user's consent, for example, from a glasssurface (a classic example of spy-work) by forensic procedures.

9

Cappelli et al. developed a commercially available syntheticfingerprint generator called SFinGe. In SFinGe, various models offingerprints are used: shape, directional map, density map, andskin deformation models (see figure). To add realism to the

image,erosion, dilation, rendering, translation, and rotation operators

areused.

Synthetic fingerprint assembly (growth)

Image synthesis

10

Methods for continuous growth from aninitial orientation map, a new synthesizedorientation map (as a recombination ofsegments of the orientation map)) using aGabor filter with polar transform have beenreported in literature. These methods alone are used to design fingerprint benchmarkswith rather complex structural features.Kuecken developed a method for synthetic fingerprint generation based onnatural fingerprint formation and modelingbased on state-of-the-art dermatoglyphics,a discipline that studies epidermal ridges onfingerprints, palms, and soles.

Synthetic fingerprint assembly (growth) using a Gabor filter with polar transform.

Image synthesis

11

Synthetic 3D (a) and 2D (b) fingerprint design based on physical modeling.

Image synthesis

12

Synthetic signatures

Current interest in signature analysis and synthesis is motivated by the

development of improved devices for human-computer interaction which

enable input of handwriting and signatures. The focus of this study is the

formal modeling of this interaction.

Similarly to signature imitation, the imitation of human handwriting is a

typical inverse problem of graphology. Automated tools for the imitation of

handwriting have been developed. It should be noted that more statistical

data, such as context information, are available in handwriting than in

signatures.

The simplest method of generating synthetic signatures is based on

geometrical models. Spline methods and Bezier curves are used for curve

approximation, given some control points. Manipulations of control points

give variations on a single curve in these methods.

13

The following evaluation properties are distinguished for syntheticsignatures: statistical, kinematical (pressure, speed of writing,

etc.),geometric, also called topological, and uncertainty (generated

images canbe intensively "infected" by noise) properties.An algorithm for signature generation based on deformation hasbeen introduced recently. Hollerbach has introduced the theoreticalbasis of handwriting generation based on an oscillatory motion

model.In Hollerbach's model, handwriting is controlled by two

independentoscillatory motions superimposed on a constant linear drift along

the lineof writing. There are many papers on the extension and

improvement ofthe Hollerbach model.

Image synthesis

14

A model based on combining shapes and physical models in

synthetic handwriting generation has been developed. Theso-called delta-log normal model was also developed. Thismodel can produce smooth connections between

characters, butcan also ensure that the deformed characters are

consistent withthe models. It was proposed to generate character shapesby Bayesian networks. By collecting handwriting examples

from awriter, a system learns the writers' writing style.

Image synthesis

15

In-class scenario: the original signature (left) and the synthetic one

(right)

Image synthesis

16

Synthetic retina and iris images

Iris recognition systems scan the surface of the iris to compare patterns. Retina recognition systems scan the surface of the retina and compare nerve patterns, blood vessels and such features.

Iris pattern painting has been used by ocularists in manufacturing

glass eyes or contact lenses for sometime. The ocularist's approach to iris synthesis is based on the composition of painted primitives, and utilized layered semi-transparent textures built from topological and optic models. These methods are widely used by today's ocularists: vanity contact lenses are available with fake iris patterns printed onto them (designed for people who want to change eye colors). Other approaches include image processing and synthesis techniques such as PCA combined with super-resolution, and random Markov field.

17

Other layer patterns can be generated based on wavelet, Fourier, polar,and distance transforms, and Voronoi diagrams. For example, Figure8.8. illustrates how a synthetic collarette topology has been designed

usinga Bezier curve in a cartesian plane. It is transformed into a concentricpattern, and superimposed with a random signal to form an irregularboundary curve.

Image synthesis

18

Synthetic speech and voice

Synthetic speech and voice have evolved considerably since the firstexperiments in the 1960s. New targets in speech synthesis includeimproving the audio quality and the naturalness of speech, developingtechniques for emotional " coloring“, and combining it withother technologies, for example, facial expressions and lip movementSynthetic voice should carry information about age, gender,emotion, personality, physical fitness, and social upbringing. A closelyrelated but more complicated problem is generating a synthetic singingvoice for training singers, studying the famous singers' styles, anddesigning synthetic user-defined styles combining voice with syntheticmusic.

19

Gait modeling

Gait recognition is defined as the identification of a person through thepattern produced by walking. The potential of gait as a biometric wasencouraged by the considerable amount of evidence available, especially inbiomechanics literature. A unique advantage of gait as biometrics is thatit has potential for recognition at a distance or at low resolution, whenother biometrics might not be perceivable. As gait is behavioural biometricsthere is much potential for within-subject variation. This includes footwear,clothing and apparel. Recognition can be based on the (static) human shapeas well as on movement, suggesting a richer recognition cue. Model-basedtechniques use the shape and dynamics of gait to guide the extraction of afeature vector.

Gait signature derives from bulk motion and shape characteristics of thesubject, articulated motion estimation using an adaptive model and motionestimation using deformable contours.

20

Parameter extraction in gait model: shape estimation (a), period estimation (b), adaptive model (c), and deformable countours (d)

Image synthesis

21

Synthetic faces

Face recognition systems detect patterns, shapes, and shadows in

the face. The reverse process - face reconstruction - is a classical

problem of criminology.

22

Modeling of facial accessories, aging, drunk, and a badly lit face (FaceGen).

23

A face model is a composition of various sub-models (eyes, nose, etc.)

The level of abstraction in face design depends on the particular application.

Traditionally, at the first phase of computer aided design, a generic(master) face is constructed. At the next phase, the necessary

attributesare added.The composition of facial sub-models is defined by a global topology

andgeneric facial parameters. The face model consists of the following

facialsub-models: eye (shape, open, closed, blinking, iris size and

movement,etc.), eyebrow (texture, shape, dynamics), mouth (shape, lip

dynamics,teeth and tongue position, etc.), nose (shape, nostril dynamics), and

ear(shape).

Image synthesis

24

Partitioning of the face into regions in the model for facial analysis and synthesis.

Image synthesis

25

Facial expressions are formed by about 50 facial muscles that

are controlled by hundreds of parameters. Psychologists

distinguish two kinds of short-time facial expressions:controlled and non-controlled facial expressions [38].Controlled expressions can be fixed in a facial model bygenerating control parameters, for example, a type of

smile.Non-controlled facial expressions are very dynamic and

arecharacterized by short time durations. The difference

betweencontrolled and non-controlled facial expressions can beinterpreted in various ways. The example belowillustrates how to use short-term facial expressions in

practice.

Image synthesis

26

The facial difference of topological information , for example, in mouthand eyebrow configurations, can be interpreted by psychologists based onthe evaluation of the first image as follows

Image synthesis

27

Decision making is based onanalysis of facial expressionchange while the person listensand responds to the question.More concretely, the local facialdifference is calculated for eachregion of the face that carriesshort-term behaviouralinformation. The local difference is defined

as achange in some reliabletopological parameter. The sum

ofweighted local differencesis the global facial difference.

The controlled and non-controlled phases of facial expressions

Image synthesis

28

Caricature is the art of making a drawing of a face which makes part of itsappearance more noticeable than it really is, and which can make a person

lookridiculous. A caricature is a synthetic facial expression, where the distances ofsome feature points from the corresponding positions in the normal face havebeen exaggerated.

Three caricatures automatically synthesized given some parameters.

Exaggerating the difference from the Mean (EDFM) is widely acceptedamong caricaturists to be the driving factor behind caricature generation.

Image synthesis

29

Examples of usage of synthetic biometrics

TestingThe commercially available synthetic fingerprints generator [5,6]

has beenused, in particular, in the Fingerprint Verification Test competitionsince 2003. An example of a tool used to create databasesfor fingerprints is SFinGe, developed at the University of Bologna(http://bias.csr.unibo.it/research/biolab/snge.html). The generateddatabases were entered in the Fingerprint Verification CompetitionFVC2004 and performed just as well as real fingerprints.

Databases of synthetic biometric informationImitation of biometric data allows the creation of databaseswith tailored biometric data without expensive studies involving

humansubjects.

30

Humanoid robots

Humanoid robots are anthropomorphic robots (have human-like shape)

that include also human-like behavioral traits. The field of humanoidrobotics includes various challenging direct and inverse biometrics.

On the other hand, in relation to inverse biometrics, robots attemptto generate postures, poses, face expressions to better communicatetheir human masters (or to each other) the internal states).Robots such as Kismet express calm, interest, disgust, happiness,surprise, etc (see (MIT,

http://www.ai.mit.edu/projects/humanoidrobotics-group/kismet/). More advanced aspects include dialogue andlogical reasoning similar to those of humans. As more robots would

enterour society it will become useful to distinguish them among each

other byrobotic biometrics.

31

Cancelable biometrics

The issue of protecting privacy in biometric systems has inspired

the area of so-called cancelable biometrics. It was first initiated by

The Exploratory Computer Vision Group at IBM T.J. WatsonResearch Center.

Cancelable biometrics aim to enhance the security and privacy of biometric authentication through generation of “deformed“ biometric data, i.e. synthetic biometrics. Instead of using a true object (finger, face), the fingerprint or face image is intentionally distorted in a repeatable manner, and this new print or image is used.

32

Synthetic biometric data in the development of a new generation of lie detectors

The features of the new generation of lie detectors include:

(a) Architectural characteristics (highly parallel configuration),

(b) Artificial intelligence support of decision making, and

(c) New paradigms (non-contact testing scenario, controlled dialogue

scenarios, flexible source use, and the possibility of interaction through an artificial intelligence supported machine-human interface).

33

Synthetic biometric data in early warning and detection system design

The idea of modeling biometric data for decision making support

enhancement at checkpoints is explored, in particular, at theBiometric Technologies Laboratory at the University of Calgary(http://enel.btlab.ucalgary.ca).

Simulators of biometric data are emerging technologies foreducational and training purposes (immigration control, bankingservice, police, justice, etc.). They emphasize decision-making

skillsin non-standard and extreme situations.

34

The next generation of non-contact lie detector system.

35

Biometric data model validation

Data generated by various models are classified as acceptable orunacceptable for further processing and use in various applications.The application-specific criteria must provide a reasonable level ofacceptability. Acceptability is defined as a set of characteristicswhich distinguish original and synthetic data. A model thatapproximates original data at reasonable levels of accuracy for thepurpose of analysis is not considered a generator of syntheticbiometric information.Artificial biometric data must be verified for their meaningfulness.The MITRE research project used synthetically generated faces tobetter understand the performance of face recognition systems. If a person'sphoto in the system's database was taken 10 years ago, is it possible toidentify the person today? A pose experiment was also conducted withsynthetic data to isolate and measure the effect of camera angle in one

degreeincrements.The modeling technique will provide an effective, more structured basisfor risk management in a large biometric system. This will help users choosethe most effective systems to meet their needs in the future.

36

Conclusions

The modeling technique will provide an effective, more structured basis for risk management in a large biometric system.

This will help users choose the most effective systems to meet their needs in the future.