Image-Based Biometric Person Authentication

Professor Heikki Kälviäinen

1 Machine Vision and Pattern Recognition Laboratory

Image-Based Biometric Person Authentication


Machine Vision and Pattern Recognition Laboratory (MVPR)

Department of Information Technology

Faculty of Technology Management

Lappeenranta University of Technology (LUT)

[email protected]

http://www.lut.fi/~kalviai

http://www.it.lut.fi/ip/research/mvpr/


Machine Vision and Pattern Recognition Laboratory

2

Content

• Machine vision and pattern recognition in LUT. • Biometric person authentication.• Face detection.• Why is detection/localization difficult.• Existing approaches.• Proposed algorithm.• Results and evaluation.• New solutions. • Conclusions.



3

Machine Vision and Pattern Recognition Laboratory (MVPR)

Leader: Prof. Heikki Kälviäinen. 2nd largest computer vision research group in Finland. Center of Excellence in Research in LUT.24 members:

• 3 Professors + 3 Post docs + 2 Visiting doctors + 11 PhD students + undergraduate students + industry coordinator.

Co-operation with 14 international universities and research institutes.Results: 18 Ph.D. degrees (and 3 externally produced), over 400 scientific

publications, 40 research projects, and spin-off companies. Objectives: 2 PhDs/year. Annual external project funding 700.000 EUR, basic funding 300.000 EUR,

total 1.0 million EUR.http://www.it.lut.fi/ip/research/mvpr/



4

MVPR Laboratory: Research Profile



5

Machine Vision System



6

Hand geometry

Iris

FingerprintsFace recognition

BiometricPerson

Authentication



7

Biometric: any measurement of a person’s physiological traits or behavior

Physiological:• Face• Fingerprint• Iris• Retinal scan• Ear shape• Hand geometry• Infrared (face, body parts)• Odor

Behavioral:• Speech• Handwriting• Signature• Lip movements• Keystroke dynamics• Gait

Genetic:• Tissue sample



8

FACEDETECT Image-Based Biometric Person Authentication

http://www.it.lut.fi/project/facedetect/

Docent, Dr. Joni Kamäräinen, Docent, Dr.Ville Kyrki, Mr. Pekka Paalanen, Mr. Jarmo Ilonen,

Prof. Heikki KälviäinenMachine Vision and Pattern Recognition Research Group

Lappeenranta University of TechnologyFINLAND

Dr. Miroslav Hamouz, Prof. Josef Kittler,

Prof. Jiri Matas

Centre for Vision, Speech, and Signal Processing (CVSSP)

University of Surrey

UNITED KINGDOM



9

Why is Face Detection Difficult?

• Object-class recognition (an object to be recognized is not a single entity rather a a group of similar objects).

• Faces exhibit significant variability in shape, colour, and texture, and may appear in arbitrary poses:

– Appearance variations over the whole population.

– Capture effects.

– Background.

• Illumination.

• Video versus still image.



10

State of the Art

• Image-based methods: – Scanning window. – Face modeled as manifolds in some high dimensional space.

Moghaddam, Pentland – probabilistic PCA

Sung and Poggio, Rowley et al.- neural networks

Osuna et al. – SVM

Viola and Jones – Adaboost on

Haar features

Jesorsky et al – Haussdorf

distance on edge images



11

State of the Art (cont.)•Feature-based methods:–Face modelled as a viable configuration of local features.

–Needs higher resolution than image-based methods.

–False alarms.

Vogelhuber, Schmid, Gaussian derivatives + angles and length ratios Weber er al., interest operator + statistical model on positionsCristinacce and Cootes, Adaboost + shape model

•Warping methods: Variability decomposed into a shape model and the model of local appearance or texture which is iteratively deformed to fit.

Cootes et al., Active Shape and Appearance modelsLades et al., Wiskott et al. Dynamic link architectures



12

Face verification (authentication) Validating a claimed identity based on the image of a face: are you Mr./Ms. X?

Face recognition (identification)Identifying a person based on an image of his/her face: who are you?

Face detection/localizationLocation of human faces in images at different positions, scales, orientations, and lighting conditions.

Introduction



13

Proposed Algorithm

• Avoiding a scanning window.• Using feature detectors.• Shape-free texture model for the final decision.



14

Feature Detector: 2-D Gabor Filter

cossin'

sincos'

),( '2''22

2

22

2

2

yxy

yxx

eef

yx fxjy

fx

f



15

Gabor Features

• Maximal joint localization in the spatial and frequency domain.• Smooth and noise tolerant.• Parameters for invariance manipulation:Frequency Envelope sharpness Orientation



16

Constructing Response Matrix

Filter response r(x,y; f,) can be calculated for variousfrequencies f and orientations to construct a response matrix.

columns represent orientationsrows represent frequenciesimage rotation appears as acircular shift of the columns

image scaling appears as ashift of the rows (highfrequencies may vanish)

A SCALE AND ROTATION INVARIANTTREATMENT OF THE RESPONSE MATRIXCAN BE ESTABLISHED, AND THUS, WECAN CONCENTRATE ONLY HOW TOCLASSIFY THEM IN THE STANDARD POSE



17

2-D Gabor Features

discrete frequency [u]

dis

cre

te fr

eq

ue

ncy

[v]

-1/2 -1/4 0 1/4 1/2

-1/2

-1/4

0

1/4

1/2

What do they ”see”?



18

Evidence Extraction

Requirements

• Scale invariant extraction.• Rotation invariant extraction.• Provides sufficiently small amount of correct candidate points. (n best points from each class; needs confidence measure).

Preferred

• Estimation of evidence scale and orientation.• Fast extraction (scalability).



19

Classifier Construction

eye

eye

nostrilnostril

eyeeye

Gaussian mixturemodel densities(EM estimation)

• Stability property guarantees approximately the Gaussian form of classes in the feature space.

• One class may still consist of several sub-clusters (open eye, closed eye, etc.).

Bayesianclassificationof features



20

Affine Learned Correspondences

Aligned images of objects andmanually selected features Variability and correspondences

1 2

3 4

5 6



21

Affine Hypothesis Search

2

2

3 11 12

4

5

2

2

1

1. Evidence extraction.

2. Affine search and match to correspon- dence model.

Instanceapproved

False alarms occur and hypothesisverification is needed



22

Face Space

• Normalization of space where shape variations and capture effects are removed from patterns.

• Based on three points on the face -> affine registration.

• Optimal with regard to the photometric variance over a big set of faces.



23

Features & Feature Detectors

• Features = salient parts of face.

• Small localization variance and frequent occurrence over population.

• Illumination, scale, rotation, and translation invariance.

• Automatic analysis using the face space desirable.



24



25



26



27



28



29

Confidence Regions

• Exhaustive search over triplets O(n)n3.

• Not all triplets have to verified, regions supporting highly likely transformations can be learned.

• Speed-up up to 1 000 times.



30

Performance Measure

rl

rleye CC

ddd

),max(

• Strict measure using the location of eye centres, not only an upright bounding box.

• deye<=0.05 in order to succeed in verification.

• deye<=0.25 corresponds to the definition of successfuldetection in the majority of state-of-the-art algorithms.C = ground truth eye center coordinates

d = distances between the detected

and ground truth ones



31

deye = 0.05



32

Recognition System



33

BANCA Database

Large realistic face and voice database collected (BANCA database):• 4 languages, each language 6540 images of 52 people.• Three scenarios simulating controlled access, office environment and

outdoor scenes.• Publicly available including a rigorous evaluation protocol.



34



35

XM2VTS Database

LABEL Rate (%)

1 56.1

2 84.2

3 70.9

4 50.9

5 84.9

6 64.2

7 70.4

8 75.5

9 54.2

10 45.8

1 triplet detected(%)

88.3

Both eye centres detected (%)

74.5



36

BioID Database

LABEL Rate (%)

1 55.6

2 67.6

3 51.2

4 39.1

5 61.1

6 54.8

7 29.5

8 34.5

9 40.0

10 48.7


73.4


48.6



37

BANCA Database

LABEL Rate (%)

1 41.4

2 60.3

3 44.5

4 44.0

5 67.4

6 34.3

7 54.8

8 63.6

9 49.6

10 61.8


81.4


44.0



38

3-dimensional Face Recognition

• 3-D images.• 3-D algorithms.• Accurate!

• Images? • Reference

databases?• Speed?



39

FACEDETECT - Publications

Hamouz, M., Kittler, J., Kamarainen, J.-K., Paalanen, P., Kälviäinen, H., Matas, J., Feature-Based Affine-Invariant Localization of Faces, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 27, No. 9, 2005, pp. 1490-1495. (Impact factor: 3.810)

Kamarainen, Joni-Kristian, Ville Kyrki, and Heikki Kälviäinen. Invariance properties of Gabor filter based features - Overview and applications. IEEE Transactions on Image Processing, Vol. 15, No. 5, 2006, pp. 1088-1099. (Impact factor: 2.428)

Kyrki, Ville, Joni-Kristian Kamarainen, and Heikki Kälviäinen. Simple Gabor feature space for invariant object recognition. Pattern Recognition Letters, Vol. 25. No. 3. 2004, pp. 311-318. (Impact factor: 1.138)

Paalanen, P., Kamarainen, J.-K., Ilonen, J., Kälviäinen, H., Feature Representation and Discrimination Based on Gaussian Mixture Model Probability Densities - Practices and Algorithms, Pattern Recognition, Vol. 39, No. 7, 2006. pp.1346-1358. (Impact factor: 2.153)



40

Conclusions and Future Work

• Algorithm successfully tested on a large face authentication data set.• Combination of features brings a significant performance boost.• Gabor jets proved as a suitable local representation of a signal.• Adequate resolution necessary for feature detectors to succeed.• 3-D face recognition much more accurate than 2-D recognition.

• Methods for non-frontal poses (more 3-D face research needed).• Speed: real-time solutions (3-D image acquisition and analysis).• Applications:

– Security applications: biometric passports, access, cash dispensers, etc.

– Surveillance applications.

Date post:	15-Jan-2016
Category:	Documents
Upload:	dirk
View:	28 times
Download:	0 times

Image-Based Biometric Person Authentication

Documents