Skip to main content
SpringerLink
Log in

Predicting Trait Impressions of Faces Using Classifier Ensembles

  • Chapter
Computational Intelligence

Part of the book series: Intelligent Systems Reference Library ((ISRL,volume 1))

Abstract

In the experiments presented in this chapter, single classifier systems and ensembles are trained to detect the social meanings people perceive in facial morphology. Exploring machine models of people’s impressions of faces has value in the fields of social psychology and human-computer interaction. Our first concern in designing this study was developing a sound ground truth for this problem domain. We accomplished this by collecting a large number of faces that exhibited strong human consensus in a comprehensive set of trait categories. Several single classifier systems and ensemble systems composed of Levenberg-Marquardt neural networks using different methods of collaboration were then trained to match the human perception of the faces in the six trait dimensions of intelligence, maturity, warmth, sociality, dominance, and trustworthiness. Our results show that machine learning methods employing ensembles are as capable as most individual human beings are in their ability to predict the social impressions certain faces make on the average human observer. Single classifier systems did not match human performance as well as the ensembles did. Included in this chapter is a tutorial, suitable for the novice, on the single classifier systems and collaborative methods used in the experiments reported in the study.

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 259.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 329.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 329.99
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. Abate, A.F., Nappi, M., Riccioa, D., Sabatinoa, G.: 2D and 3D face recognition: A survey. Pattern Recognit. Lett. 14, 1885–1906 (2007)

    Article  Google Scholar 

  2. Albright, L., Malloy, T.E., Dong, Q., Kenny, D.A., Fang, X.: Cross-cultural consensus in personality judgments. J. Personal and Soc. Psychol. 3, 558–569 (1997)

    Article  Google Scholar 

  3. Alcock, D., Solano, J., Kayson, W.A.: How individuals’ responses and attractiveness influence aggression. Psychol. Rep. 3(2), 1435–1438 (1998)

    Google Scholar 

  4. Alpaydin, E.: Introduction to machine learning. MIT Press, Cambridge (2004)

    Google Scholar 

  5. Alt\(\imath \)nçay, H., Demirekler, M.: An information theoretic framework for weight estimation in the combination of probabilistic classifiers for speaker identification. Speech Commun. 4, 255–272 (2000)

    Google Scholar 

  6. Bellman, R.: Adaptive control process: A guided tour. Princeton University Press, Princeton (1961)

    MATH  Google Scholar 

  7. Berry, D.S., Brownlow, S.: Were the physiognomists right? Personal and Soc. Psychol. Bull. 2, 266–279 (1989)

    Article  Google Scholar 

  8. Berry, D.S., McArthur, L.Z.: Perceiving character in faces: The impact of age-related craniofacial changes on social perception. Psychol. Bull. 1, 3–18 (1986)

    Article  Google Scholar 

  9. Brahnam, S.: Modeling physical personalities for virtual agents by modeling trait impressions of the face: A neural network analysis. The Graduate Center of the City of New York, Department of Computer Science, New York (2002)

    Google Scholar 

  10. Breiman, L.: Bagging predictors. Mach. Learn. 2, 123–140 (1996)

    Google Scholar 

  11. Breiman, L.: Random forest. Mach. Learn. 1, 5–32 (2001)

    Article  Google Scholar 

  12. Bruce, V.: Recognising faces. Lawrence Erlbaum Associates Publishers, London (1988)

    Google Scholar 

  13. Brunelli, R., Poggio, T.: Hyperbf networks for gender classification. In: DARPA Image Understanding Workshop, pp. 311–314 (1992)

    Google Scholar 

  14. Brunelli, R., Poggio, T.: Face recognition: Features versus templates. IEEE Trans. Pattern Anal. and Mach. Intell. 10, 1042–1052 (1993)

    Article  Google Scholar 

  15. Brunswik, E.: Perception and the representative design of psychological experiments. University of California Press, Berkeley (1947)

    Google Scholar 

  16. Bull, R., Rumsey, N.: The social psychology of facial appearance. Springer, Heidelberg (1988)

    Google Scholar 

  17. Burton, A.M., Bruce, V., Dench, N.: What’s the difference between men and women? Evidence from facial measurement. Percept. 2, 153–176 (1993)

    Article  Google Scholar 

  18. Chellappa, R., Wilson, C.L., Sirohey, S.: Human and machine recognition of faces: A survey. Proceedings of the IEEE, pp. 705–740 (1995)

    Google Scholar 

  19. Cottrell, G.W., Fleming, M.K.: Face recognition using unsupervised feature extraction. In: International Conference on Neural Networks, pp. 322–325 (1990)

    Google Scholar 

  20. Cottrell, G.W., Metcalfe, J.: EMPATH: Face, emotion, and gender recognition using holons. In: Touretzky, D. (ed.) Adv. Neural Inf. Process Syst., pp. 564–571. Morgan & Kaufman, San Mateo (1991)

    Google Scholar 

  21. Cover, T.M., Hart, P.E.: Nearest neighbor pattern classificiation. IEEE Trans. Inf. Theory 1, 21–27 (1967)

    Article  Google Scholar 

  22. Cristianini, N., Shawe-Taylor, J.: An introduction to support vector machines and other kernel-based learning methods. Cambridge University Press, Cambridge (2000)

    Google Scholar 

  23. Czyz, J., Kittler, J., Vandendorpe, L.: Multiple classifier combination for face-based identity verification. Pattern Recognit. 7, 1459–1469 (2004)

    Article  Google Scholar 

  24. Duda, R.O., Hart, P.E., Stork, D.G.: Pattern Classification. Wiley, New York (2000)

    Google Scholar 

  25. Eagly, A.H., Ashmore, R.D., Makhijan, M.G., Longo, L.C.: What is beautiful is good, but ...: A meta-analytic review of research on the physical attractiveness stereotype. Psychol. Bull. 1, 109–128 (1991)

    Article  Google Scholar 

  26. Edelman, B.E., Valentin, D., Abdi, H.: Sex classification of face areas: How well can a linear neural network predict human performance. J. Biol. Syst. 3, 241–264 (1998)

    Article  Google Scholar 

  27. Efron, B.: The jackknife, the bootstrap and other resampling plans. SIAM, Philadelphia (1982)

    Google Scholar 

  28. Enlow, D.H., Hans, M.G.: Essentials of facial growth. W. B. Saunders Company, Philadelphia (1996)

    Google Scholar 

  29. Feingold, A.: Good-looking people are not what we think. Psychol. Bull. 2, 304–341 (1992)

    Article  MathSciNet  Google Scholar 

  30. Freierman, S.: Constructing a real-life mr. potato head. Faces: The ultimate composite picture. The New York Times D:6 (2000)

    Google Scholar 

  31. Golumb, B.A., Lawrence, D.T., Sejnowshi, T.J.: Sexnet: A neural network identifies sex from human faces. Adv. Neural Inf. Process Syst., 572–577 (1991)

    Google Scholar 

  32. Guo, G., Li, S.Z., Chan, K.L.: Support vector machines for face recognition. Image and Vis. Comput., 631–638 (2001)

    Google Scholar 

  33. Heisele, B., Ho, P., Poggio, T.: Face recognition with support vector machines: Global versus component-based approach. In: The Eighth IEEE International Conference on Computer Vision, Vancouver, BC, pp. 688–694 (2001)

    Google Scholar 

  34. Ho, T.K.: The random subspace method for constructing decision forests. IEEE Trans. Pattern Anal. and Mach. Intell. 8, 832–844 (1998)

    Google Scholar 

  35. Jain, A., Huang, J.: Integrating independent components and linear discriminant analysis for gender classification. In: Sixth IEEE International Conference on Automatic Face and Gesture Recognition, pp. 159–163 (2004)

    Google Scholar 

  36. Jain, A.K., Dubes, R.C., Chen, C.C.: Bootstrap techniques for error estimation. IEEE Trans. Pattern Anal. and Mach. Intell. 5, 628–633 (1987)

    Article  Google Scholar 

  37. Jain, A.K., Duin, R.P.W., Mao, J.: Statistical pattern recognition: a review. IEEE Trans. Pattern Anal. and Mach. Intell. 1, 4–37 (2000)

    Article  Google Scholar 

  38. Kanghae, S., Sornil, O.: Face recognition using facial attractiveness. In: The 2nd International Conference on Advances in Information Technology, Bangkok, Thailand (2007)

    Google Scholar 

  39. Keating, C.F., Mazur, A., Segall, M.H.: A cross-cultural exploration of physiognomic traits of dominance and happiness. Ethol. and Sociobiol., 41–48 (1981)

    Google Scholar 

  40. Kittler, J.: On combining classifiers. IEEE Trans. Pattern Anal. and Mach. Intell. 3, 226–239 (1998)

    Article  Google Scholar 

  41. Kohonen, T.: Associative memory: A system theoretic approach. Springler, Berlin (1977)

    Google Scholar 

  42. Kosugi, M.: Human-face search and location in a scene by multi-pyramid architecture for personal identification. Syst. and Comput. Jpn. 6, 27–38 (1995)

    Article  Google Scholar 

  43. Kuncheva, L.I.: Clustering-and-selection model for classier combination. In: Knowledge-Based Intelligent Engineering Systems and Allied Technologies, Brighton, pp. 185–188 (2000)

    Google Scholar 

  44. Kuncheva, L.I.: Combining pattern classifiers: Methods and algorithms. Wiley, New York (2004)

    Book  MATH  Google Scholar 

  45. Kuncheva, L.I.: Diversity in multiple classifier systems. Inf. Fusion 1, 3–4 (2005)

    Article  Google Scholar 

  46. Kuncheva, L.I., Whitaker, C.J.: Measures of Diversity in Classifier Ensembles and their Relationship with the ensemble accuracy. Mach. Learn. 2, 181–207 (2003)

    Article  Google Scholar 

  47. Langlois, J.H., Kalakanis, L., Rubenstein, A.J., Larson, A., Hallam, M., Smoot, M.: Maxims or myths of beauty? A meta-analytic and theoretical review. Psychol. Bull. 3, 390–423 (2000)

    Article  Google Scholar 

  48. Lanitis, A., Taylor, C.J., Cootes, T.F.: Automatic interpretation and coding of face images using flexible models. IEEE Trans. Pattern Anal. and Mach. Intell. 7, 743–756 (1997)

    Article  Google Scholar 

  49. Lanitis, A., Taylor, C.J., Cootes, T.F.: Toward Automatic Simulation of Aging Effects on Face Images. IEEE Trans. Pattern Anal. and Mach. Intell. 4, 442–455 (2002)

    Article  Google Scholar 

  50. Levenberg, K.: A Method for the solution of certain nonlinear problems in least squares. Q Appl. Math. 2, 164–168 (1944)

    MATH  MathSciNet  Google Scholar 

  51. Ling, C.X., Huang, J., Zhang, H.: Auc: A better measure than accuracy in comparing learning algorithms. In: Canadian Conference on Artificial Intelligence 2003, Halifax, Canada, pp. 329–341 (2003)

    Google Scholar 

  52. Lu, X., Jain, A.K.: Ethnicity identification from face images. In: SPIE: Biometric Technology for Human Identification Conference: Biometric Technology for Human Identification, Orlando, FL, pp. 114–123 (2004)

    Google Scholar 

  53. Marquardt, D.: An algorithm for least-squares estimation of nonlinear parameters. SIAM J. Appl. Math., 431–441 (1963)

    Google Scholar 

  54. Martínez-Muñoz, G., Suárez, A.: Switching class labels to generate classification ensembles. Pattern Recognit. 10, 1483–1494 (2005)

    Google Scholar 

  55. Martinez, A.M., Benavente, R.: The ar face database. CVC Technical Report #24 (1998), http://rvl1.ecn.purdue.edu/~aleix/aleix_face_DB.html

  56. McArthur, L.Z., Baron, R.M.: Toward an ecological theory of social perception. Psychol. Rev. 3, 215–238 (1983)

    Article  Google Scholar 

  57. Melville, P., Mooney, R.J.: Constructing diverse classifier ensembles using artificial training examples. In: International Joint Conferences on Artificial Intelligence, pp. 505–510 (2003)

    Google Scholar 

  58. Metz, C.E.: Basic principles of ROC analysis. Semin. Nucl. Med. 4, 283–298 (1978)

    Article  Google Scholar 

  59. Mitsumoto, S.T., Kawai, H.: Male/female identification from 8 x 6 very low resolution face images by a neural network. Pattern Recognit. 2, 331–335 (1996)

    Google Scholar 

  60. Moghaddam, B., Yang, M.-H.: Gender classification with support vector machines. In: Sixth IEEE International Conference on Automatic Face and Gesture Recognition (FG), pp. 306–311 (2000)

    Google Scholar 

  61. Moghaddam, B., Yang, M.-H.: Learning gender with support faces. IEEE Trans. Pattern Anal. and Mach. Intell. 5, 306–311 (2002)

    Google Scholar 

  62. Mulford, M., Orbell, J., Shatto, C., Stockard, J.: Physical attractiveness, opportunity, and success in everyday exchange. American J. Sociol. 6, 1565–1592 (1998)

    Article  Google Scholar 

  63. Nocedal, J., Wright, S.J.: Numerical optimization. Springer, New York (1999)

    Book  MATH  Google Scholar 

  64. O’Toole, A.J., Abdi, H., Deffenbacher, K.A., Bartlett, J.C.: Classifying faces by race and sex using an autoassociative memory trained for recognition. In: 13th Annual Conference on Cognitive Science, Hillsdale, NJ, pp. 847–851 (1991)

    Google Scholar 

  65. O’Toole, A.J., Deffenbacher, K.A.: The perception of face gender: The role of stimulus structure in recognition and classification. Mem. and Cogn., 146–160 (1997)

    Google Scholar 

  66. Oja, E.: Subspace Methods of Pattern Recognition. Research Studies Press Ltd., Letchworth (1983)

    Google Scholar 

  67. Oja, E.: Principal components, minor components and linear neural networks. Neural Netw., 927–935 (1992)

    Google Scholar 

  68. Opitz, D., Maclin, R.: Popular ensemble methods: an empirical study. J. Artif. Intell. Res., 169–198 (1999)

    Google Scholar 

  69. Padgett, C., Cottrell, G.W.: A simple neural network models categorical perception of facial expressions. In: Proceedings of the 20th Annual Cognitive Science Conference, Madison, WI, pp. 806–807 (1998)

    Google Scholar 

  70. Phillips, P.J.: Support vector machines applied to face recognition. Adv. Neural Inf. Process Syst., 803–809 (1998)

    Google Scholar 

  71. Rosenberg, S.: New approaches to the analysis of personal constructs in person perception. In: Land, A.L., Cole, J.K. (eds.) Nebraska symposium on motivation, pp. 179–242. University of Nebraska Press, Lincoln (1977)

    Google Scholar 

  72. Rowley, H.A., Shumeet, B., Kanade, T.: Neural network-based face detection. IEEE Trans. Pattern Anal. and Mach. Intell. 1, 23–38 (1998)

    Article  Google Scholar 

  73. Russell, S., Norvig, P.: Artificial intelligence: A modern approach. Prentice Hall, Upper Saddle River (2002)

    Google Scholar 

  74. Sirovich, L., Kirby, M.: Low dimensional procedure for the characterization of human faces. J. Opt. Soc. Am. 3, 519–524 (1987)

    Article  Google Scholar 

  75. Swets, D.L., Weng, J.: Using discriminant eigenfeatures for image retrieval. IEEE Trans. Pattern Anal. and Mach. Intell. 8, 831–837 (1996)

    Article  Google Scholar 

  76. The MathWorks, Using MATLAB: The language of technical computing. The Mathworks, Inc., Natick, MA (2000)

    Google Scholar 

  77. Todd, J.T., Mark, L.S.: The perception of human growth. Sci. Am. 2, 132–144 (1980)

    Article  Google Scholar 

  78. Turk, M.A., Pentland, A.P.: Eigenfaces for recognition. J. Cogn. Neurosci. 1, 71–86 (1991)

    Article  Google Scholar 

  79. Turk, M.A., Pentland, A.P.: Face recognition using eigenfaces. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Silver Spring, MD, pp. 586–591 (1991)

    Google Scholar 

  80. Valentin, D., Abdi, H., Edelman, B.E., O’Toole, A.J.: Principal component and neural network analyses of face images: What can be generalized in gender classification? J. Math. Psychol. 4, 398–413 (1997)

    Article  Google Scholar 

  81. Valentin, D., Abdi, H., O’Toole, A.J.: Categorization and identification of human face images by neural networks: A review of the linear autoassociative and principal component approaches. J. Biol. Syst. 3, 413–429 (1994)

    Article  Google Scholar 

  82. Valentin, D., Abdi, H., O’Toole, A.J., Cottrell, G.W.: Connectionist models of face processing: A survey. Pattern Recognit. 9, 1209–1230 (1994)

    Article  Google Scholar 

  83. van der Heijden, F., Duin, R.P.W., de Ridder, D., Tax, D.M.J.: Classification, parameter estimation, and state estimation: An engineering approach using MATLAB. John Wiley & Sons, Ltd., Chichester (2004)

    Book  MATH  Google Scholar 

  84. Vapnik, V.N.: The Nature of Statistical Learning Theory. Springer, New York (1995)

    MATH  Google Scholar 

  85. Wechsler, H., Gutta, S., Philips, P.J.: Gender and ethnic classification of Face Images. In: 3rd Int. Conf. on Automatic Face and Gesture Recognition, Nara, Japan, pp. 194–199 (1998)

    Google Scholar 

  86. Whitaker, C.J., Kuncheva, L.I.: Examining the relationship between majority vote accuracy and diversity in bagging and boosting (2003), http://www.informatics.bangor.ac.uk/kuncheva/papers/lkcw_tr.pdf

  87. Zebrowitz, L.A.: Reading faces: Window to the soul? Westview Press, Boulder (1998)

    Google Scholar 

  88. Zebrowitz, L.A., Montepare, J.M.: Impressions of babyfaced individuals across the life span. Dev. Psychol. 6, 1143–1152 (1992)

    Article  Google Scholar 

  89. Zebrowitz, L.A., Montepare, J.M.: Social Psychological Face Perception: Why Appearance Matters. Soc. and Personality Psychol. Compass 3, 1497–1517 (2008)

    Article  Google Scholar 

  90. Zebrowitz, L.A., Montepare, J.M., Lee, H.K.: They don’t all look alike: Individuated impressions of other racial groups. J. Personal and Soc. Psychol. 1, 85–101 (1993)

    Article  Google Scholar 

  91. Zenobi, G., Cunningham, P.: Using diversity in preparing ensembles of classifiers based on different feature subsets to minimize generalization error. In: 12th Conference on Machine Learning, pp. 576–587 (2001)

    Google Scholar 

  92. Zhao, W., Chellappa, R., Phillips, P.J., Rosenfeld, A.: Face recognition: A literature survey. ACM Comp. Surv. 4, 399–458 (2000)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Computer Information Systems, Missouri State University, 901 S. National, Springfield, MO 65804, USA

    Sheryl Brahnam

  2. DEIS, IEIIT—CNR, Università di Bologna, Viale Risorgimento 2, 40136, Bologna, Italy

    Loris Nanni

Authors
  1. Sheryl Brahnam
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Loris Nanni
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. School of Computer Science, Cardiff University , 5 The Parade,Roath, CF24 3AA, Cardiff, UK

    Christine L. Mumford

  2. University of South Australia Adelaide , Mawson Lakes Campus, South Australia, Australia

    Lakhmi C. Jain

Rights and permissions

Reprints and permissions

Copyright information

© 2009 Springer-Verlag Berlin Heidelberg

About this chapter

Cite this chapter

Brahnam, S., Nanni, L. (2009). Predicting Trait Impressions of Faces Using Classifier Ensembles. In: Mumford, C.L., Jain, L.C. (eds) Computational Intelligence. Intelligent Systems Reference Library, vol 1. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-01799-5_12

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-01799-5_12

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-01798-8

  • Online ISBN: 978-3-642-01799-5

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation