1、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 being developed in two directions:Analysis for ident
2、ification and recognition of humans (direct problems) and Synthesis 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
3、,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 numb
4、ers to create data that are statistically equivalent to real data. For example, in face modeling, a number of ethnic or race models can be used to represent ethnic diversity, the specific ages and genders of individuals, and other parameters for simulating a variety of tests.,7,Image synthesis,The i
5、mage synthesis-based approach falls into the area of computer graphics, a very- well explored area with application from forensics (face reconstruction) to computer animation. A taxonomy for the creation of physics-based and empirically derived models for the creation of statistical distributions of
6、 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 an individuals presentation of biometric information. For example, a facial image can be
7、 camouflaged with glasses, beards, wigs, make-up, etc. Sensor factors include resolution, noise, and sensor age, and can be expressed using physics-based or geometry-based equations. This factor is also relevant to the skills of the user of the system. Environmental factors affect the quality of col
8、lected data. For example, light, smoke, fog, rain or snow can affect the acquisition of visual band images, degrading the biometric facial recognition algorithm. High humidity or temperature can affect infrared images. This environmental influence affects the acquisition of fingerprint images differ
9、ently for different types of fingerprint sensors.,8,Synthetic fingerprints,Albert Wehde was the first to “forge“ fingerprints in the 1920s. Wehde “designed“ and manipulated the topology of synthetic fingerprints at the physical level. The forgeries were of such high quality that professionals could
10、not recognize them. Todays interest in automatic fingerprint synthesis addresses the urgent problems of testing fingerprint identification systems, training security personnel, biometric database security, and protecting intellectual property. Traditionally, two possibilities of fingerprint imitatio
11、n are discussed with respect to obtaining unauthorized access to a system: (i) the authorized user provides his fingerprint for making a copy, and (ii) a fingerprint is taken without the authorized users consent, for example, from a glass surface (a classic example of spy-work) by forensic procedure
12、s.,9,Cappelli et al. developed a commercially available synthetic fingerprint generator called SFinGe. In SFinGe, various models of fingerprints are used: shape, directional map, density map, and skin deformation models (see figure). To add realism to the image, erosion, dilation, rendering, transla
13、tion, and rotation operators are used.,Synthetic fingerprint assembly (growth),Image synthesis,10,Methods for continuous growth from an initial orientation map, a new synthesized orientation map (as a recombination of segments of the orientation map) using a Gabor filter with polar transform have be
14、en reported in literature. These methods alone are used to design fingerprint benchmarks with rather complex structural features. Kuecken developed a method for synthetic fingerprint generation based on natural fingerprint formation and modeling based on state-of-the-art dermatoglyphics, a disciplin
15、e that studies epidermal ridges on fingerprints, 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
16、 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 handw
17、riting 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
18、 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 synthetic signatures: statis
19、tical, kinematical (pressure, speed of writing, etc.), geometric, also called topological, and uncertainty (generated images can be intensively “infected“ by noise) properties. An algorithm for signature generation based on deformation has been introduced recently. Hollerbach has introduced the theo
20、retical basis of handwriting generation based on an oscillatory motion model. In Hollerbachs model, handwriting is controlled by two independent oscillatory motions superimposed on a constant linear drift along the line of writing. There are many papers on the extension and improvement of the Holler
21、bach model.,Image synthesis,14,A model based on combining shapes and physical models in synthetic handwriting generation has been developed. The so-called delta-log normal model was also developed. This model can produce smooth connections between characters, but can also ensure that the deformed ch
22、aracters are consistent with the models. It was proposed to generate character shapes by Bayesian networks. By collecting handwriting examples from a writer, a system learns the writers writing style.,Image synthesis,15,In-class scenario: the original signature (left) and the synthetic one (right),I
23、mage 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 ma
24、nufacturing glass eyes or contact lenses for sometime. The ocularists 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 todays ocularists: vanity conta
25、ct 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 base
26、d on wavelet, Fourier, polar, and distance transforms, and Voronoi diagrams. For example, Figure 8.8. illustrates how a synthetic collarette topology has been designed using a Bezier curve in a cartesian plane. It is transformed into a concentric pattern, and superimposed with a random signal to for
27、m an irregular boundary curve.,Image synthesis,18,Synthetic speech and voice,Synthetic speech and voice have evolved considerably since the first experiments in the 1960s. New targets in speech synthesis include improving the audio quality and the naturalness of speech, developing techniques for emo
28、tional “ coloring“, and combining it with other technologies, for example, facial expressions and lip movement Synthetic voice should carry information about age, gender, emotion, personality, physical fitness, and social upbringing. A closely related but more complicated problem is generating a syn
29、thetic singing voice for training singers, studying the famous singers styles, and designing synthetic user-defined styles combining voice with synthetic music.,19,Gait modeling,Gait recognition is defined as the identification of a person through the pattern produced by walking. The potential of ga
30、it as a biometric was encouraged by the considerable amount of evidence available, especially in biomechanics literature. A unique advantage of gait as biometrics is that it has potential for recognition at a distance or at low resolution, when other biometrics might not be perceivable. As gait is b
31、ehavioural biometrics there is much potential for within-subject variation. This includes footwear, clothing and apparel. Recognition can be based on the (static) human shape as well as on movement, suggesting a richer recognition cue. Model-based techniques use the shape and dynamics of gait to gui
32、de the extraction of a feature vector.Gait signature derives from bulk motion and shape characteristics of the subject, articulated motion estimation using an adaptive model and motion estimation using deformable contours.,20,Parameter extraction in gait model: shape estimation (a), period estimatio
33、n (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, an
34、d 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,
35、 the necessary attributes are added. The composition of facial sub-models is defined by a global topology and generic facial parameters. The face model consists of the following facial sub-models: eye (shape, open, closed, blinking, iris size and movement, etc.), eyebrow (texture, shape, dynamics),
36、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
37、 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 by generating control parameters, for example, a type of smile. Non-controlled facia
38、l expressions are very dynamic and are characterized by short time durations. The difference between controlled and non-controlled facial expressions can be interpreted in various ways. The example below illustrates how to use short-term facial expressions in practice.,Image synthesis,26,The facial
39、difference of topological information , for example, in mouth and eyebrow configurations, can be interpreted by psychologists based on the evaluation of the first image as follows,Image synthesis,27,Decision making is based on analysis of facial expression change while the person listens and respond
40、s to the question. More concretely, the local facial difference is calculated for each region of the face that carries short-term behavioural information. The local difference is defined as a change in some reliable topological parameter. The sum of weighted local differences is the global facial di
41、fference.,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 its appearance more noticeable than it really is, and which can make a person look ridiculous. A caricature is a synthetic facial expres
42、sion, where the distances of some feature points from the corresponding positions in the normal face have been exaggerated.,Three caricatures automatically synthesized given some parameters.,Exaggerating the difference from the Mean (EDFM) is widely accepted among caricaturists to be the driving fac
43、tor behind caricature generation.,Image synthesis,29,Examples of usage of synthetic biometrics,Testing The commercially available synthetic fingerprints generator 5,6 has been used, in particular, in the Fingerprint Verification Test competition since 2003. An example of a tool used to create databa
44、ses for fingerprints is SFinGe, developed at the University of Bologna (http:/bias.csr.unibo.it/research/biolab/snge.html). The generated databases were entered in the Fingerprint Verification Competition FVC2004 and performed just as well as real fingerprints.Databases of synthetic biometric inform
45、ation Imitation of biometric data allows the creation of databases with tailored biometric data without expensive studies involving human subjects.,30,Humanoid robots,Humanoid robots are anthropomorphic robots (have human-like shape) that include also human-like behavioral traits. The field of human
46、oid robotics includes various challenging direct and inverse biometrics.On the other hand, in relation to inverse biometrics, robots attempt to generate postures, poses, face expressions to better communicate their human masters (or to each other) the internal states). Robots such as Kismet express
47、calm, interest, disgust, happiness, surprise, etc (see (MIT, http:/www.ai.mit.edu/projects/humanoidrobotics- group/kismet/). More advanced aspects include dialogue and logical reasoning similar to those of humans. As more robots would enter our society it will become useful to distinguish them among
48、 each other by robotic 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. Watson Research Center.Cancelable biometrics aim to e
49、nhance 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.
50、,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 dialoguescenarios, flexible source use, and the possibility of interaction through an artificial intelligence supported machine-human interface).,
