Skip to main content
SpringerLink
Log in

Hybrid Systems for Facial Analysis and Processing Tasks

  • Chapter
Soft Computing for Image Processing

Part of the book series: Studies in Fuzziness and Soft Computing ((STUDFUZZ,volume 42))

  • 289 Accesses

Abstract

We have proposed in this chapter a hybrid architecture for forensic classification and retrieval tasks and we have shown its feasibility using FERET — a large data base of facial images. The specific facial analysis and processing tasks considered herein include (i) surveilling a gallery of images for the presence of specific probes, (ii) contents-based image retrieval (CBIR), (iii) CBIR subject to correct ID (‘match’) displaying specific facial landmarks such as wearing glasses, (iv) gender classification, and (v) ethnic classification. The hybrid architectures, consisting of an ensemble of connectionist networks — radial basis functions (RBF) — and inductive decision trees (DT), combines the merits of ‘holistic’ template matching with those of ‘discrete’ features and classifiers using both positive and negative learning examples. The specific characteristics of our hybrid architecture include (a) query by consensus as provided by ensembles of networks for coping with the inherent variability of the image formation and data acquisition process, (b) categorical classifications using decision trees, and (c) flexible and adaptive thresholds as opposed to ad hoc and hard thresholds.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 129.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 169.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Gudivada, V.N., Raghavan, V.V. (1995): Content-Based Image Retrieval Systems, IEEE Compter18, 18–21

    Article  Google Scholar 

  2. Jain, A. (1992): Final Report to NSF on the Workshop of Visual Information Management

    Google Scholar 

  3. Samal, A., Iyengar, P. (1992): Automatic Recognition and Analysis of Human faces and Facial Expressions: A Survey, Pattern Recognition 25,65–77.

    Article  Google Scholar 

  4. Chellappa, R. Wilson, C.L., Sirohey, S. (1995): Human and Machine Recognition of Faces: A Survey, Proc. IEEE 83, 705–740.

    Article  Google Scholar 

  5. Gutta, S. Wechsler, H. (1997): Face Recognition using Hybrid Classifiers. Pattern Recognition 30(4), 539–553.

    Article  Google Scholar 

  6. Turk, M. Pentland, A. (1991): Eigenfaces for Recognition, J Cognitive Neuroscience 3, 71–86.

    Article  Google Scholar 

  7. Sirohey, S.A. (1993): Human Face Segmentation and Identification, Computer Vision Laboratory, University of Maryland CS-TR-3176

    Google Scholar 

  8. Arad, N., Dyn, N., Reisfeld, D., Yeshurun, Y. (1994): Image Warping by Radial Basis Functions: Application to Facial Expressions, CVGIP: Graphical Models and Image Processing 56(2), 161–172

    Article  Google Scholar 

  9. Takacs, B., Wechsler, H. (1995): Face Location Using a Dynamic Model of Retinal Feature Extraction, Proc. of International Workshop on Automatic Face- and Gesture Recognition (IWAFGR), Zurich, Switzerland, 243–247

    Google Scholar 

  10. Brunelli, R., Poggio, T. (1993): Face Recognition: Features versus Templates, IEEE Trans. on Pattern Analysis and Machine Intelligence 15(4), 1042–1052

    Article  Google Scholar 

  11. Baron, R.J. (1981): Mechanisms of Human Facial Recognition, International Journal of Man-Machine Studies 15,137–178

    Article  Google Scholar 

  12. Huber, P. J. (1981): Robust statistics, John Wiley, New York

    Book  MATH  Google Scholar 

  13. Phillips, P.J. (1994) Matching Pursuit Filter Design, Proc. of the International Conference on Pattern Recognition (ICPR), Jerusalem, Isreal, 3,57–61

    Google Scholar 

  14. Buhmann, J., Lange, J., Malsburg, C, Vorbruggen, J.C., Wurtz, R.P. (1992): Object Recognition with Gabor Functions in the Dynamic Link Architecture. In: Kosko, B. (Ed.): Neural Networks for Signal Processing, Prentice Hall, New York, 1,121–159

    Google Scholar 

  15. Valentin, D., Abdi, H., Toole, A., Cottrell, G.W. (1994): Connectionist Models of Face Processing: A Survey, Pattern Recognition 27( 9), 1209–1230

    Article  Google Scholar 

  16. Golomb, B.A., Lawrence, D.T., Sejnowski, T.J. (1990): SEXNET: A Neural Network Identifies Sex from Human Faces. In: Lippmann, R.P., Moody, J.E., Touretzky, D.S. (Eds.): Advances in Neural Information Processing Systems (NIPS), Morgan Kaufmann, Palo Alto, 3,572–577

    Google Scholar 

  17. Brunelli, R., Poggio, T. (1993): Caricatural Effects in Automated Face Perception, Journal of Biological Cybernetics 69, 235–241

    Article  Google Scholar 

  18. Burton, A.M., Bruce, V., Dench, N. (1993): What’s the Difference between Men and Women? Evidence from Facial Measurement, Perception 22,153–176

    Google Scholar 

  19. Tamura, S., Kawai, H., Mitsumoto, H. (1996): Male/Female Identification from 8x6 Very Low Resolution Face Images by Neural Network, Pattern Recognition 29(2), 331–335

    Article  Google Scholar 

  20. Wiskott, L. (1997): Phantom Faces for Face Analysis, Pattern Recognition 30(6), 837–846

    Article  Google Scholar 

  21. Bezdek, J.C. (1994): What is Computational Intelligence ?. In: Zurada, J.M., Marks, C, Robinson, J. (Eds.): Computational Intelligence, Prentice Hall, New York, 1–12

    Google Scholar 

  22. Medsker, L.R. (1995): Hybrid Intelligent Systems, Kluwer Academic Publishers, Massachusetts

    Book  MATH  Google Scholar 

  23. Gallant, S.I. (1988): Connectionist Expert Systems, Communications of the ACM, 31(2), 152–169

    Article  Google Scholar 

  24. Fu, L.M. (1993): Knowledge-based Connectionism for Revising Domain Theories, IEEE Transactions on Systems, Man, and Cybernetics, 23(1), 173–182

    Article  Google Scholar 

  25. Towell, G.G., Shavlik, J.W. (1994): Refining Symbolic Knowledge using Neural Networks. In: Michalski, R.S., Tecuci, G. (Eds.): Machine Learning: A Multistrategy Approach, Morgan Kaufmann, Palo Alto, 4,405–438

    Google Scholar 

  26. Hampshire, J. B., Waibel, A. (1992): The Meta-Pi Network: Building Distributed Knowledge Representations for Robust Multisource Pattern Recognition, IEEE Trans, on Pattern Analysis and Machine Intelligence, 14(7), 751–769

    Article  Google Scholar 

  27. Battiti, R., Colla, A.M. (1994): Democracy in Neural Nets: Voting Schemes for Classification, Neural Networks, 7(4), 691–707

    Article  Google Scholar 

  28. Bala, J., Wechsler, H. (1996): Shape Analysis using Multistrategy Learning, Pattern Recognition, 8(3), 67–79

    Google Scholar 

  29. Chang, E.I., Lippmann, R.P. (1991): Using Genetic Algorithms to improve Pattern Classification Performance. In: Touretzky, D.S. (Ed.): Advances in Neural Information Processing Systems (NIPS), Morgan Kaufmann, Palo Alto, 2, 380–387

    Google Scholar 

  30. Specht, D.F. (1990): Probabilistic Neural Networks, Neural Networks 3,109–118

    Article  Google Scholar 

  31. Pao, Y.H. (1989): Adaptive Pattern Recognition and neural Networks, Addison Wesley, Massachusetts

    MATH  Google Scholar 

  32. Lincoln, W.P., Skrzypek, J. (1990): Synergy of Clustering Multiple Back Propagation Networks. In: Touretzky, D.S. (Ed.): Advances in Neural Information Processing Systems (NIPS), Morgan Kaufmann, Palo Alto, 1,650–657

    Google Scholar 

  33. Soulie, F.F., Viennet, E., Lamy, B. (1993): Multi-Modular Neural Network Architectures for Pattern Recognition: Applications in Optical Character Recognition and Human Face Recognition, International Journal of Pattern Recognition and Artificial Intelligence (IJPRAI), 7,487–498.

    Article  Google Scholar 

  34. Robertson, G., Craw, I. (1994): Testing Face Recognition Systems, Image and Vision Computing, 19,609–614

    Article  Google Scholar 

  35. Phillips, P.J., Wechsler, H., Huang, J., Rauss, P. (1998): The FERET Database and Evaluation Procedure for Face Recognition Algorithms, Image and Vision Computing, 5, 36–41

    Google Scholar 

  36. Pentland, A., Picard, R.W., Sclaroff, S. (1994): Photobook: Tools for Content-Based Manipulation of Image Databases. In: Proceedings of Storage and retrieval for Image and Video Databases (SPIE), 2(185), 34–37

    Article  Google Scholar 

  37. Nelson, C. (1993): Intelligent Facial Recognition Systems, Final Report prepared for the United States Department of Energy Office of Safeguards and Security

    Google Scholar 

  38. Huang, J., Gutta, S., Wechsler, H. (1996): Detection of Human Faces using Decision Trees, Proc. of 2nd International Conference on Automatic Face and Gesture Recognition (IWAFGR), Killington, Vermont, 248–252

    Google Scholar 

  39. Gutta, S., Huang, J., Kakkad V., and Wechsler, H. (1998): Face Surveillance, Proc. of the International Conference on Computer Vision (ICCV), Mumbai, India, 298–303

    Google Scholar 

  40. Haykin, S. (1999): Neural Networks, 2nd Edition, Prentice Hall, New Jersey

    MATH  Google Scholar 

  41. Quinlan, J.R. (1986): The Effect of Noise on Concept Learning. In: Michalski, R.S., Carbonell, J.G., Mitchell, T.M. (Eds.): Machine Learning: an Artificial Intelligence Approach, Morgan Kaufmann, Palo Alto, 2,149–166

    Google Scholar 

  42. Weiss, S.M., Kulikowski, C.A. (1991): Computer Systems That Learn, Morgan Kaufmann, Palo Alto (1991)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Philips Reasearch Labs, 345 Scarborough Road, Briarcliff Manor, NY, 10510, USA

    Srinivas Guttat

  2. Department of Computer Science, George Mason University, Fairfax, VA, 22030, USA

    Harry Wechsler

Authors
  1. Srinivas Guttat
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Harry Wechsler
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Machine Intelligence Unit, Indian Statistical Institute, 203 B. T. Road, Calcutta, 700035, India

    Sankar K. Pal , Ashish Ghosh  & Malay K. Kundu ,  & 

Rights and permissions

Reprints and permissions

Copyright information

© 2000 Springer-Verlag Berlin Heidelberg

About this chapter

Cite this chapter

Guttat, S., Wechsler, H. (2000). Hybrid Systems for Facial Analysis and Processing Tasks. In: Pal, S.K., Ghosh, A., Kundu, M.K. (eds) Soft Computing for Image Processing. Studies in Fuzziness and Soft Computing, vol 42. Physica, Heidelberg. https://doi.org/10.1007/978-3-7908-1858-1_18

Download citation

  • DOI: https://doi.org/10.1007/978-3-7908-1858-1_18

  • Publisher Name: Physica, Heidelberg

  • Print ISBN: 978-3-7908-2468-1

  • Online ISBN: 978-3-7908-1858-1

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics

Search

Navigation