recognition of human faces: from biological to artifical vision massimo tistarelli computer vision...

Recognition of Human Recognition of Human Faces: from Biological Faces: from Biological

to Artifical Visionto Artifical Vision

Massimo TistarelliMassimo TistarelliComputer Vision Laboratory

University of Sassari – Italytista@uniss.it

October 30 2007 - IstanbulMassimo Tistarelli 2

The Computer Vision Lab

Started in 1990 at University of Genova

Moved to Alghero, Sardinia in 2002

Second European Conference on Computer Vision – May 1992 – ECCV 1992 – S. Margherita Ligure, Italy

Int.l Workshop on Facial Image Analyisis and Recognition Technology – May 1998 – Freiburg, Germany

Int.l Workshop on Biometric Authentication – June 2002 Copenhagen, Denmark

Int.l Summer School for Advanced Studies on Biometrics:– 1st Authentication and Recognition – June 2-6 2003– 2nd Multimodality and System Integration – June 6-10 2005– 3rd New Sensors, Databases and Evaluation – June 12-16 2006– 4th New Technologies and Embedded Systems – June 11-15 2007– 5th New Technologies for Security and Privacy – June 9-13 2008

Fifth IEEE AutoId Workshop – June 7-8 2007, Alghero, Italy

Third IAPR/IEEE Int.l Conference on Biometrics – June 2-5 2009, Alghero, Italy

Credits

The laboratory staff:

– Manuele Bicego

– Gavin Brelstaff

– Linda Brodo

– Enrico Grosso

– Dakshina Kisku (on leave from IITK)

– Andrea Lagorio

– Ajita Rattani (joint Phd with UNICA)

– Elif Surer

Concise outline

Recognition of human faces and Biometrics

Issues from the Human Visual System

Applied technologies for face recognition

Exploiting knowledge from biological systems

New “trends and hints” ?

Biometrics

•Biometrics (as in statistics): “Statistical study

of biological observations and phenomena”

(Webster)

•Biometrics (as in security industry): “A

measurable, physical characteristic or personal

behavioral trait used to recognize the identity, or

verify the claimed identity, of an enrollee”

•Biometric recognition: Personal recognition

based on “who you are or what you do” as

opposed to “what you know” (password) or

“what you have” (ID card)

Most natural for humans

Highly acceptable and non-intrusive

Highly applicable:

– Static identity verification

– Uncontrolled face detection and identification from video

Medium to Low performances

Not unique (twins)

Aging and time effects

Why face recognition?

Are both from the same Are both from the same subject?subject?

How do we recognize faces?

Automated face recognition

Face Detection and Selection

Facial featuresextraction

Registrationand

Representation

Matching(comparison)

Input (grabbed) sequence

Individual face model

Automatic classification of faces

A class separation problem:

False Acceptance

= (Fh,Fk)

Identification from human faces

A class separation problem:

• Choice of optimal representation • Inter-class similarity vs Intra-class variability

• Two different people may have very similar appearance

Twins Father and son

www.marykateandashley.com news.bbc.co.uk/hi/english/in_depth/americas/2000/us_elections

Inter-class SIMILARITY

Intra-class VARIABILITY

Recognition/Identification and Authentication/Verification

Many facts come into play:

– The dimensionality of the data and feature space

– The noise in the data

– The data alignment/localizazion

– The acquisition device

– The algorithmic classification

– The time variability of the data

– MORE….

Data dimensionality

... Not many ... (20x14)

It’s more a question of spatial distribution and …

proper frequency tuning

How many pixels should be processed to reliably How many pixels should be processed to reliably recognize a face?recognize a face?

Local analysis (Receptive fields)

The density and size of the cones decreases linearly from the retina center (fovea) to the periphery

Contextual analysis (Visual attention)

Eye movements while watching a girl’s face (A.L. Yarbus, “Eye Movements and Vision”, Plenum Press, 1967)

Space-variant representations

J. Bigun et al. 1997

M. Tistarelli et al. 1996

Where is the “Mom’s face” neuron?

Brain anatomy

Coronal anatomical image of a brain:STS, Superior temporal sulcus

MTG, middle temporal gyrus

ITS, inferior temporal sulcus

ITG, inferior temporal gyrus

FG, fusiform gyrus

FFrom rom fMRIfMRI, f, face-selective neurons have been found in the ace-selective neurons have been found in the inferior inferior temporal areastemporal areas, the , the superior temporal sensory areasuperior temporal sensory area, the , the amygdalaamygdala, the , the ventral striatumventral striatum ( (receivingreceiving input from the amygdala) and the input from the amygdala) and the inferior inferior convexityconvexity..

Specific regions have been reported also in the fusiform gyrus responding Specific regions have been reported also in the fusiform gyrus responding significantly to viewing faces.significantly to viewing faces. A. R. Damasio, J. Damasio and G. W. Van Hoesen. “Prosopagnosia: “Prosopagnosia:

Anatomic basis and behavioral mechanisms”. Anatomic basis and behavioral mechanisms”. Neurology, vol. 32, 331–341, 1982.

Brain activation

• Parts of the Parts of the inferiorinferior and and medialmedial temporal cortextemporal cortex may work may work together to process faces,together to process faces, the the amygdalaamygdala being being responsible for responsible for assigning significance to faces, assigning significance to faces, and thus and thus affecting both affecting both attention and mnemonic attention and mnemonic aspects of face processingaspects of face processing..

• Autism and Asperger syndromAutism and Asperger syndrom

C. A. Nelson. “The development and neural bases of face recognition”. “The development and neural bases of face recognition”. Infant and Child Development, vol. 10, 3-18, 2001.J. P. Aggleton, M. J. Burton and R. E. Passingham. “Cortical and subcortical afferents to the amygdala of the rhesus monkey “Cortical and subcortical afferents to the amygdala of the rhesus monkey (Macaca mulatta)”. (Macaca mulatta)”. Brain Research, vol. 190, 347–368, 1980.

Brain activation – fMRI maps

C. L. Leveroni et al. “Neural Systems Underlying the Recognition of Familiar and Newly Learned Faces”, The Journal of Neuroscience, January 15, 2000, 20(2):878–886

Recognition of 50 Familiar Faces (FF) vs 50 Newly Learned Faces (NL) and compared to rejection of 50 Foil (FO -False Objective) faces.Encoding (EN) session for learning new faces.

Brain activation areas

C. L. Leveroni et al. “Neural Systems Underlying the Recognition of Familiar and Newly Learned Faces”, The Journal of Neuroscience, January 15, 2000, 20(2):878–886

Facial features and brain activation

Aylward et al. Brain Activation during Face Perception: Evidence of a Developmental Change. J. Cogn. Neurosci..2005; 17: 308-319.

Eyes vs radialAll stimuli

Facial features and brain activation

Vaina, L.M., Solomon, J., Chowdhury, S., Sinha, P., Belliveau, J.W., “Functional Neuroanatomy of Biological Motion Perception in Humans”. Proc. of the National Academy of Sciences of the United States of America, Vol. 98, No. 20 (Sep. 25, 2001) , pp. 11656-11661

Face and motion perception

Biological Motion

Non Rigid Motion

Neural architecture of face perception - 1

J.V. Haxby, E.A. Hoffman, and M.I. Gobbini. “The distributed human neural system for face perception”. Trends in Cognitive Sciences, vol. 4, No. 6, 223--233, June 2000

Motion

Static

A. O’Toole, D.A. Roark, and H. Abdi. “ Recognizing moving faces: A psychological and neural synthesis”.Trends in Cognitive Science, 6:261--266, 2002.

Cooperative/competitive visual tracking AND recognition

Evidence

Face trackingprocess

Feature extractionregistration process

Incrementalmatching process

Expectation

Biological face recognition

Face representation

It is not clear how faces are represented in the HVS, but the representation is formed by a dynamically updated collection of visual fixations.– Both foveal and peripheral vision are involved, the former

responsible for a more accurate representation.J.M. Henderson et al. “Eye movements are functional during face learning”, Memory & Cognition

2005, 33 (1), 98-106.D.R. Melmoth et al. “The Effect of Contrast and Size Scaling on Face Perception in Foveal and

Extrafoveal Vision”, Investigative Ophthalmology and Visual Science. 2000;41:2811-2819.

This representation includes both iconic data as well as information about the spatial relationship among face elements.

– What about a “face template”?

Pose-dependent

Algorithms

Pose-invariant

Pose-dependency

Matching features

Appearance-based (Holistic)PCA, LDA, ICA, LPPKernel . . .

Feature-based (Analytic)Gabor setsLBP . . .

HybridLFAEGBM/JETsAAM . . .

Viewer-centered Images

Object-centered Models

Face representation

A step back:Taxonomy of face recognition Algorithms

• Gordon et al., 1995 3D from 2D

• Lengagne et al., 1996 3D from stereo

• Atick et al., 1996 LFA

• Yan et al., 1996 Geometric modeling

• Zhao et al., 2000 Shape from Shading • Zhang et al., 2000 3D from video

Automatic face recognition

Face recognition involves several sequential processes:

Face detection

Facial featureslocalization

Registration/Representation

Classification

Normalization

Face detection

Sebastian Marcel - http://www.idiap.ch/~marcel/demos/comparison/opencv/alt/image_66-detect.jpg

The Viola-Jones face detector

• At present the most efficient face detector

implemented and largely adopted

• Publicly available within the Open-CV library

• Several improvements are being developed

P. Viola, M. J. Jones, "Robust Real-Time Face Detection", International Journal Computer Vision, 57(2), pp. 137-154, 2004.

Some improvements…

Improve localization accuracy by post-filtering the face region

Face detection

Classification

Normalization

THE Problem

Illumination compensation

Original ImageHistogram equalization

Image re-lighting

),(),(),( yxLyxRyxI ),(

),(),(

yxIyxR

dxdyLLdxdyyxIyxLyxLF yx )()),(),()(,()( 222

L(x,y)

Isotropic diffusion (Gaussian filtering)

Anisotropic diffusion (Lagrange solution of (1))

R. Gross and V. Brajovic, “An Image Preprocessing Algorithm for Illumination Invariant Face Recognition”, International Conference on Audio- and Video-Based Biometric Person Authentication, 2003.

D. Jobson, Z. Rahmann and G. Woodell, “A Multiscale Retinex for Bridging the Gap Between Color Images and the Human Observations of Scenes”, IEEE Transanctions on Image Processing, volume 6, Issue 7, 1997.

Face detection

Classification

Normalization

Understanding Facial features

Gray level oriented patterns/photometric properties

Physical Landmarks

Human face structure

Cranial circumferenceMax. cranial breadthMin. frontal breadthBigonial breadthUpper facial heightBasion-Prosthion lengthNasal breadth (max.)Lower nasal breadthOrbital breadthBiorbital breadthForamen magnum breadthCranial heightMax. cranial lengthBizygomatic breadthTotal facial heightBasion-Nasion lengthBasal heightUpper nasal breadthOrbital heightInterorbital breadthPalate-external breadth & lengthPalate-internal breadth & length…. (more)

Craniometric measurements (1)

Facial Region

L_eye R_eye Nose Mth L_ear R_ear Chn L_chk R_chk F_head

Mean percentage of times (averaged across viewers) that each facial region was fixated at least once.

How to define facial features?

J.H. Henderson et al. “Gaze Control for Face Learning and Recognition by Humans and Machine”; in T. Shipley and P. Kellman (Eds.), From Fragments to Objects: Segmentation and Grouping in Vision

Facial features as 2D/3D landmarks

• 2D landmarks can be defined and tracked on face images

• Simple 2D vs complex 3D representations

• The LFA-based approach (Local Feature Analyisis) uses localized kernels, which are constructed from PCA-based eigenvectors, for extracting pre-defined topographic facial features (e.g., eyebrows, cheek, mouth, etc.)

Marked average face image

•Five topographic kernels are shown in the top row

•Five corresponding residual correlations (response) in the bottom row.

Facial features as 2D/3D landmarks

Facial features as 2D patterns

Gabor wavelets• Provide a description of the local

structure of the facial patterns

• Convolution with a bank of frequency-tuned filters

Local Binary Patterns (LBP)

• Pixels are labeled by thresholding the 3x3neighbourhood with the center value and considering the result as a binary number.

• The histogram of the labels is used as a texture descriptor.

Facial features as 2D patterns

Face detection

Classification

Normalization

Face displacement and perception

… … But the data is still thereBut the data is still there

In cognitive psychology it is called “perceptual organization”

The “registration” problem

S. Arca, P. Campadelli, and R. Lanzarotti. A face recognition system based on automatically determined facial fiducial points. Pattern Recognition, 39(3):432–443, 2006.

Face detection

Classification

Normalization

Linear Linear algorithmalgorithm

SVM, MPM, PCA, CCA, FDA…

DataData Embed dataEmbed data

if data described by numerical vectors: embedding ~ (non-linear) transformation

Kernel methods and learning

Support Vector Machines

Support Vector Machines are Support Vector Machines are binarybinary classifiers classifiers

Class 1Class 1

Class 2Class 2V. Vapnik, S.E. Golowich, A.J. Smola: Support Vector Method for Function Approximation, Regression Estimation and Signal Processing. Neural Information Processing Systems 1996: 281-287

The separating hyperplane can be very complex

Problems may occur with outliers

The shape of the hyperplane depends on the population of the classes

Need for an “impostor” class

Support Vector Machines

One-Class Support Vector Machines are One-Class Support Vector Machines are unaryunary classifiers classifiers

One-Class Support Vector Machines

Class 1Class 1

ImpostorsImpostorsBen-Hur, A., Horn, D., Siegelmann, H., , Vapnik, V.: « Support vector clustering ». Journal of Machine Learning Research 2 (2001) 125–137

The separating surface is a hyperspehere

Selectivity can be adjusted by two parameters

No need for direct “impostor” training

...122 iRaxi

5 .5 0 %N O N EO n e - C la s s S V M

6 .1 9 %M i x e d d a ta s e t

6 .0 0 %Y a l e

5 .5 0 %S ti r li n g

6 .0 0 %B e rnB in a r y S V M

E q u a l E r r o r R a te

(E E R )I m p o s to r s e tM e t h o d

5 .5 0 %N O N EO n e - C la s s S V M

6 .1 9 %M i x e d d a ta s e t

6 .0 0 %Y a l e

5 .5 0 %S ti r li n g

6 .0 0 %B e rnB in a r y S V M

E q u a l E r r o r R a te

(E E R )I m p o s to r s e tM e t h o d

0 . 5 6 %O n e th r e s h o l d p e r

s u b j e c t

3 . 3 8 %

1 2 2 5 2 02 2 7 5

S in g l e th r e s h o l d

E q u a l E r r o r R a t e

( E E R )I m p o s t o r t e s t sC l ie n t t e s t sT h r e s h o ld

0 . 5 6 %O n e th r e s h o l d p e r

s u b j e c t

3 . 3 8 %

1 2 2 5 2 02 2 7 5

S in g l e th r e s h o l d

E q u a l E r r o r R a t e

( E E R )I m p o s t o r t e s t sC l ie n t t e s t sT h r e s h o ld

Poor Poor representationrepresentation

(10 frames/sbj)(10 frames/sbj)

Rich Rich representationrepresentation

(100 (100 frames/sbj)frames/sbj)

Test Test ParameterFalse Reject

Rate False Accept

Fingerprint

FVC[2004]

Exaggerated distortion 2% 2%

FpVTE[2003]

US govt. operational data 0.1% 1%

FRVT[2002]

Varied lighting, outdoor/indoor

10% 1%

FRGC[2006]

Time lapse, varied lighting/expression,

outdoor/indoor10% 0.1%

IrisITIRT

[2005] Indoor environment,

multiple visits0.99% 0.94%

VoiceNIST

[2004]Text independent, multi-

lingual5-10% 2-5%

Face recognition performances

Human vs Machine face recognition performances

A. O’Toole, J. Phillips, F. Jiang, Ayyad, Pénard & A. O’Toole, J. Phillips, F. Jiang, Ayyad, Pénard & AbdiAbdi, “Benchmarking Algorithms Against Humans” , “Benchmarking Algorithms Against Humans” IEEE:T-PAMI, 2007 IEEE:T-PAMI, 2007 (in press)(in press) Identity Matching for Easy Face Pairs

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

False Accept Rate

ficati

Algorithm B

Algorithm D

Viisage

Algorithm C

Algorithm A

Human Performance

Chance Performance

Identity Matching for Difficult Face Pairs

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

False Accept Rate

ficati

Viisage

Human Performance

Algorithm A

Algorithm B

Algorithm C

Algorithm D

Chance Performance

We are still HERE!

…Where is the “Mom’s face” neuron?

WHERE DO WE GO NEXT?

Face detection

Classification

Normalization

Where do we go next?

ATTENTIVE AND DYNAMIC

PROCESSES

What was missing?What was missing?

A simple recognition experiment…

Visual attention

Eye movements while watching a girl’s face (A.L. Yarbus, “Eye Movements and Vision”, Plenum Press, 1967)

The analysis of visual tracking patterns is sometimes misleading…

R.C. Miall, University Laboratory of Physiology, Oxford, UKJohn Tchalenko, Camberwell College of Arts, London, UK

Visual tracking

Subject-specific representation

For recognition we are not interested on what faces have in common but rather what differentiate one face from another.

For localization and tracking we are interested on what every face has in common (to tell a face from “non-faces”)

SVMfeature space

Distinctive patches

Face 1Face 2

Confusion: most similar patches

Distinctive patches

Bicego M., Brelstaff G., Brodo L., Grosso E., Lagorio A. and Tistarelli M. (2007) “Distinctiveness of faces: a computational approach”, ACM Transactions on Applied Perception, Vol. 5, n. 2, 2008.(in press)

Three examples of differences extracted from pairs of images of the same person. The 100 most weighted patches are

(A) perceptual and (B) computational test results of saliency

of local facial features. Red and green points in (A) represent the two positions selected by each of the 45 subjects (13 male, 32 female).

(A) perceptual and (B) computational test results of saliency

of local facial features. Red and green points in (A) represent the two positions selected by each of the 45 subjects (13 male, 32 female).

52 subjects from the BANCA database (26 M and 26 F).

Matched Controlled (MC) BANCA protocol.

The Weighted Error Rate (WER) is computed from the threshold from G1 on G2 (26 subjects each).

Image areas are selectively deleted from the face image.

Authentication test

Quantitative results by selectively masking image areas

Comparative tests

Lowe [2004] (SIFT),

Salah et al. [2002],

Walther [2006],

Ullman et al. [2002].

Authentication test

Hidden Markov Models

Statistical analysis of sequences of patterns

… …

Modelling (learning) sequences of symbols

– capture the underlying structure of a set of symbol strings

– stochastic generalisation of finite-state automata, where both transitions between states and generation of output symbols are governed by probabilistic distributions

Markovian Models, where states are not directly observable

– a probability function is associated to each state describing the probability that a symbol is emitted from this state.

Coding– the image is divided into a sequence of T sub-images

– for each sub-image the DCT coefficients are computed

– only the most important D coefficients are retained

– the final sequence is composed by DxT symbols

Learning : train one HMM for each subject:– the number of states is fixed a priori

– at the end we have one HMM for each subject

Recognition : – Bayesian scheme: assuming a priori equi probable classes, the object

is assigned to the highest likelihood class

Recognition and selective attention

Starting point: HMM based classification of faces

“Walking on the face” for obtaining HMM sequences

Standard raster scan-path Saliency-based scan-path

Attention drives face

scanning

Model of attention-based classification

Inhibition of Return

Face sampleSaliency Master Map Feature

MapsNormalization and Integration

Hidden Markov Model

'What'Information

Feature Extraction

content

location 'Where'Information

Associative Level

Attentive Level

classification

Experiments on BANCA protocol MC

Gabor wavelets for saliency map construction

Employed features: gray levels, DCT coefficients, Haar

wavelets

A. A. Salah, M. Bicego, L. Akarun, E. Grosso, M. Tistarelli: "Hidden Markov model-based face recognition using selective attention", Human Vision and Electronic Imaging XII, Proc. of SPIE, vol. 6492, (2007)

Authentication test

About 40% of the saccades are sufficient to obtain the same result of raster scan

In some cases further saccades may decrease the classification performances

The system can be improved by exploiting also the where information

Authentication test

Face recognition from video

Dynamics in a video stream conveys far more Dynamics in a video stream conveys far more information than a collection of single snapshotsinformation than a collection of single snapshots

Individual motion can be highly distinctiveIndividual motion can be highly distinctive

… … and even more distinctiveand even more distinctive

Advantages from video:

• More data available• Temporal integration• Behavioral cues• Spatial and temporal sampling

Effects of motion on representation

Facerotation

Facescaling

Facetranslation

Face sub-spaces manifold

The curse of dimensionality…

…the risk is to have too much data to be processed

How to exploit the added information in video?How to exploit the added information in video?

Standard VGA (640x480)

1 frame: 300 KByte30 frames: 1 MByte

Standard video: ~1 MByte/Sec

Not just more data to be processed:

Data selection (pose, expression, illumination, noise…)

Multi-data fusion (decision/score/feature level)

3D reconstruction/virtual views

Resolution enhancement

Expression and emotion analysis

Behavioral analysisBehavioral analysis

DynamicDynamic video templates video templates…?

A. O’Toole, D.A. Roark, and H. Abdi. “ Recognizing moving faces: A psychological and neural synthesis”.Trends in Cognitive Science, 6:261--266, 2002.

How to represent photometric and dynamic How to represent photometric and dynamic information at the same time?information at the same time?

Statistical analysis of sequences of patterns

… …

Standard HMMs can be extended to multi-dimensional patterns and sequences … in many ways

1. Each image is modeled as a single HMM and the sequence of images as a sequence of HMMs (A. Hadid and M. Pietikainen. “An experimental investigation about the integration of facial dynamics in video-based face recognition”. Electronic Letters on Computer Vision and Image Analysis, 5(1):1-13, 2005.)

2. The entire video is modeled as a single HMM

(X. Liu and T. Chen. “Video-based face recognition using adaptive hidden Markov models”. In Proc. Int. Conf.

on Computer Vision and Pattern Recognition, 2003.)

3. The images and the sequence itself are modeled as a complex, hierarchical HMM-based structure (M. Bicego, E.Grosso, M. Tistarelli. “Person authentication from video of faces: a behavioral and physiological approach using Pseudo Hierarchical Hidden Markov Models”, Int.l Conference on Biometric Authentication 2006, Hong Kong, China, January 2006. )

Dynamic Hidden Markov Models

Physiological and behavioral featuresPhysiological and behavioral features

HMMHMM

PH-HMMPH-HMM

Pseudo Hyerarchical HMM

Pseudo-Hierarchical Pseudo-Hierarchical Hidden Markov ModelHidden Markov Model

The image stream is represented as a collection of sub-streams, each corresponding to a different facial expression or motion.

The change in expression is capturede by the transition matrix of the PH-HMM

Pseudo Hyerarchical HMM

Recogniton from video: performances

Classification Results (recognition rate)Still images: one HMM for class 11/21 (52.38%)

Still images: one HMM for cluster of class 14/21 (66.67%)

Still images: one HMM for image 12/21 (57.14%)

Video: Pseudo Hierarchical HMM 21/21 (100%)_________________________________________

Verification Results (EER)Client tests: 20 - Impostor tests: 420

Still images: one HMM for class 20.24%

Still images: one HMM for cluster of class 10.60%

Still images: one HMM for image 13.81%

Video: Pseudo Hierarchical HMM 6.07 %

M. Bicego, E. Grosso, M. Tistarelli: "Person Authentication from video of faces: a behavioral and physiological approach using Pseudo Hierarchical Hidden Markov Models" . Int. Conf. On Biometrics (ICBA06) , LNCS 3832, D. Zhang, A.K. Jain Eds, pp 113-120, (2006)

Industrial FR systemsA4Vision, Inc. (3D scanner)

AcSys Biometrics Corp.

Animetrics Inc. (3D shape)

C-VIS Computer Vision und Automation GmbH (Neural Networks)

Cognitec Systems GmbH (LFA)

Cybula Ltd. (3D shape/2D texture)

DreamMirh Co., Ltd. (2D ??? )

Geometrix, Inc. (3D shape)

Iconquest (2D Fractal-based ??? )

Identix Inc. (LFA)

Imagis Technologies Inc. (VISIPHOR Advanced Face Recognition ???)

Neven Vision, Inc. (2D Gabor wavelets; feature-based )

Takumi Vision Technologies, Inc. (Hi-Tech algorithms ??? )

Viisage (Template matching ??? )

VisionSphere Technologies Inc. (2D features with Holistic Feature Code)

Source: www.face-rec.org

Near-infra red imaging

Authentimetric F1

Conclusion

• Many algorithmic and system solutions have been proposed.

• More investments needed.

• More basic research on open issues.

• Need for “universal” databases for evaluation and testing on key applications.

New technologies for Security and Privacy

Contact: tista@uniss.it

http://biometrics.uniss.it

5th Int.l Summer School for Advanced Studies on

Biometrics for secure authentication:

Alghero, Italy - June, 9 – 13 2008

Contact: tista@uniss.it

http://icb07.uniss.it

3rd IAPR/IEEE Int.l Conference onBiometrics

Alghero, Italy - June, 2 – 5 2009

recognition of human faces: from biological to artifical vision massimo tistarelli computer vision...

personal recognition

recognition technology

enrolleebiometric recognition

new technologies

fka class separation

human facesfkfhl1l2s1s3s2

claimed identity

biological systemsnew

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