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How reliable is the result?

2. 1. 4. 3. How reliable is the result?. Who are these people? Are you sure?. Ground truth Accurate landmark Inaccurate landmark. 2. 1. 4. 3. Defining Reliability. Estimation based on intensity model of each landmark. Intrinsic precision. A. Martinez (2002)

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How reliable is the result?

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  1. 2 1 4 3 How reliable is the result? • Who are these people? • Are you sure?

  2. Ground truth Accurate landmark Inaccurate landmark 2 1 4 3 Defining Reliability Estimation based on intensity model of each landmark Intrinsicprecision A. Martinez (2002) IEEE Transactions on Pattern Analysis and Machine Intelligence,, 24(6):748–763,

  3. 2 1 4 3 Mutual Information • Intrinsic precision • Reliability • Reliability estimate • Maximize information given by the estimate:

  4. 2 1 4 3 Examples from IOF-ASM Reliable Unreliable

  5. 2 1 4 3 Examples from IOF-ASM Reliable Unreliable

  6. 2 1 4 3 Examples from IOF-ASM Reliable Unreliable

  7. 2 1 4 3 Examples from IOF-ASM

  8. 2 1 4 3 Reliability of a shape Outlier threshold (unacceptable error) Intrinsic precision (average error)

  9. 2 1 4 3 Incremental accumulation of evidence

  10. 2 1 4 3 Incremental accumulation of evidence Undefined Reliable Unreliable

  11. 2 1 4 3 Segmentation results: IOF-ASM 2214 images 1.92 (±0.01) pixavg 146 images 3.67 (±0.18) pixavg 2360 images XM2VTS database Avg p2c error = 2.03 (±0.02) pix

  12. 2 1 4 3 Segmentation results: ASM 2141 images 2.69 (±0.04) pixavg 219 images 6.80 (±0.72) pixavg 2360 images XM2VTS database Avg p2c error = 3.06 (±0.08) pix

  13. 2 1 4 3 Application I: Automatic model selection ?

  14. 2 1 4 3 Application I: Automatic model selection Confusion Matrices (color-coded) Accuracy: 89.6 % Accuracy: 82.1 %

  15. 2 1 4 3 Application II: Reliable Identification XM2VTS Database w/ BAD initialization

  16. 2 1 4 3 Application II: Reliable Identification

  17. 2 1 4 3 Conclusions on Reliability Estimation • 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

  18. 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 PosevariantImage 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

  19. 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 viewindependentfacial 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.

  20. 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 ScatiLabs) 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 ScatiLabs). 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.

  21. Automatic Face Recognition demo

  22. Automatic Face Recognition demo

  23. 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

  24. Acknowledgements

  25. THE END

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