How reliable is the result?

Who are these people?

Are you sure?

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1

Defining Reliability

Ground truthAccurate

landmarkInaccurate

landmark

0)(ˆ

1)(ˆ)(

)(

j

j

lr

lr

Estimation based on intensity model of each landmark

Intrinsic precision

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2

A. Martinez (2002)IEEE Transactions on Pattern Analysis and Machine Intelligence,, 24(6):748–

763,

Mutual Information

Intrinsic precision

Reliability

Reliability estimate

Maximize information given by the estimate:

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3

Examples from IOF-ASM

Reliable

Unreliable

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4

Examples from IOF-ASM

Reliable

Unreliable

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5

Examples from IOF-ASM

Reliable

Unreliable

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6

Examples from IOF-ASM

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7

Reliability of a shape

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Intrinsic precision (average error)

Outlier threshold(unacceptable error)

8

Incremental accumulation of evidence

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9

Incremental accumulation of evidence

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Reliable Unreliable

Undefined10

Segmentation results: IOF-ASM

2214 images1.92 (±0.01) pix avg

146 images3.67 (±0.18) pix

avg

2360 imagesXM2VTS database

Avg p2c error = 2.03 (±0.02) pix

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11

Segmentation results: ASM

2141 images2.69 (±0.04) pix avg

219 images6.80 (±0.72) pix

avg

2360 imagesXM2VTS database

Avg p2c error = 3.06 (±0.08) pix

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Application I: Automatic model selection

?

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Application I: Automatic model selection

Accuracy: 89.6 % Accuracy: 82.1 %

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14

Confusion Matrices (color-coded)

Application II: Reliable Identification

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XM2VTS Databasew/ BAD initialization

15

Application II: Reliable Identification

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Conclusions on Reliability Estimation

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High correlation of proposed measure with accuracy

Generic approach for ASM methods Only requirement is a local metric for each

landmark Does not introduce changes in the

algorithms

Very low false positives rate

Useful to provide robustness to biometric systems Based on consistency with training data

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Journal publications

F.M. Sukno, S. Ordas, C. Butakoff, S. Cruz, and A.F. Frangi. Active shape models with invariant optimal features: Application to facial analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence, 29(7):1105-1117, 2007.

F.M. Sukno and A.F. Frangi. Reliability Estimation for Statistical Shape Models. Conditionally accepted for publication in IEEE Transactions on Image Processing, pending minor revision

F.M. Sukno, J.J. Guerrero and A.F. Frangi. Projective Active Shape Models for Posevariant Image Analysis of Quasi-Planar Objects: Application to Facial Analysis. Submitted for publication

C. Hoogendoorn, F.M. Sukno, S. Ordas, and A.F. Frangi. Bilinear Models for Spatiotemporal Point Distribution Analysis: Application to Extrapolation of Left Ventricular, Biventricular and Whole Heart Cardiac Dynamics. Submitted for publication

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Conferences A. Ortega, F.M. Sukno, E. Lleida, A.F. Frangi, A. Miguel, L. Buera, and E. Zacur.AV@CAR: A

spanish multichannel multimodal corpus for in-vehicle automatic audiovisual speech recognition. In Proc. 4th Int. Conf. on Language Resources and Evaluation, Lisbon, Portugal, volume 3, pages 763-767. (www.cilab.upf.edu/ac), 2004.

A. Ortega, F.M. Sukno, E. Lleida, A.F. Frangi, A. Miguel, L. Buera, and E. Zacur. Base de datos audiovisual y multicanal en castellano para reconocimiento automático del habla multimodal en el automóvil. In III Jornadas en Tecnologías del Habla, pages 125-130, (www.cilab.upf.edu/ac), 2004.

F.M. Sukno, S. Ordas, C. Butakoff, S. Cruz, and A.F. Frangi. Active shape models with invariant optimal features (IOF-ASMs). In Proc. 5th Int. Conf. on Audio- and Video-Based Biometric Person Authentication, New York, NY, USA. Lecture Notes in Computer Science vol. 3546, pages 365-375, 2005.

F.M. Sukno, J.J. Guerrero and A.F. Frangi. Homographic active shape models for viewindependent facial analysis. In Proc. SPIE Biometric Technologies for Human Identification, Orlando, FL, USA, volume 5779, pages 152-163, 2005.

F.M. Sukno and A.F. Frangi. Exploring reliability for automatic identity verification with statistical shape models. In Proc. IEEE Workshop on Automatic Identification Advanced Technologies, Alguero, Italy, pages 80-86, 2007.

D. González-Jiménez, F.M. Sukno, J.L. Alba-Castro and A.F. Frangi. Automatic pose correction for local feature-based face authentication. In Proc. 4th IEEE Workshop on Motion of Non-Rigid and Articulated Objects, Mallorca, Spain. Lecture Notes in Computer Science vol. 4069, pages 356-365, 2006.

C. Hoogendoorn, F.M. Sukno, S. Ordas, and A.F. Frangi. Bilinear models for spatiotemporal point distribution analysis: Application to extrapolation of whole heart cardiac dynamics. In Proc. IEEE ICCV 2007 8th Int. Workshop on Mathematical Methods in Biomedical Image Analysis, Rio de Janeiro, Brazil,, 2007.

19

Projects BIOSECURE: Biometrics for Secure Authentication (IST-2002-507534)

European Excellence Network of FP6/2002/IST/1. Comisión Europea. www.biosecure.info

iE-VULTUS: Desarrollo de un sistema centralizado de biometría facial de tercera generación para el control de acceso y seguridad en entornos inteligentes (Proyecto Coordinado TIC2002-04495-C02). Ministerio de Ciencia y Tecnología

HERMES: Análisis biométrico de actividades óculo-faciales con técnicas de modelado estadístico robusto para sistemas de asistencia a la conducción segura de vehículos, (Plan Nacional de I+D+i, Proyectos de Investigación Aplicada TEC2006-03617/TCM). Ministerio de Educación y Ciencia.

iEYE: (en conjunto con Scati Labs) Definición de un sistema de tercera generación para seguridad en entornos inteligentes mediante técnicas de visión por ordenador (Programa de Fomento de la Investigación Tecnológica PROFIT FIT-070000-2002-935, FIT-070200-2003-112, FIT-390000-2004-30, Proyecto Iberoeka IBK 02-263). Ministerio de Industria, Turismo y Comercio.

eMedusa: (en conjunto con Scati Labs). Estrategias de adquisición, análisis, vi-sualización y fusión de información y su integración en un sistema avanzado de seguridad para entornos complejos. Programa de Fomento de la Investigación Tecnológica (PROFIT/Iberoeka FIT-360000-2006-55, FIT-390000-2007-30). Ministerio de Industria, Turismo y Comercio. 20

Automatic Face Recognition demo

21

Automatic Face Recognition demo

22

Conclusions IOF-ASM demonstrated consistently superior to ASM

Different databases with frontal images (30% more accurate)

Multi-view databases (70% more accurate)

The coplanar face model w/ PASM Adds robustness to head rotations Requires stronger image intensity models

Average performance of ASM methods is acceptable - Adding reliability estimates: Helps to automatically discards outliers Allows for model selection and convergence assessment

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Acknowledgements

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THE END

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