Abstract
An X Windows software tool for the construction of faces with a weighted combination of eigenvectors is described. The eigenvectors were extracted from an autoassociative matrix that comprised 100 face images. The program input consists of eigenvectors and sets of weights that describe individual faces and combines these to create face images. The tool creates a panel of buttons that permits the display of individual eigenvectors and the display of an average face as well. Facilities for on-line changes to the intensity of individual eigenvectors can be used to change the appearance of a face. Previously, O’Toole, Abdi, Deffenbacher, and Bartlett (1991) have shown that the intensity of certain individual eigenvectors contains reliable information for determining the sex and race of the face.
Article PDF
Similar content being viewed by others
References
Bahrick, H. P., Bahrick, P. O., &Wittlinger, R. P. (1975). Fifty years of memory for names and faces: A cross-sectional approach.Journal of Experimental Psychology: General,104, 54–75.
Davies, G. (1982). Composite systems for recalling faces: “Helping police with their inquiries?” In A. Trankell (Ed.),Reconstructing the past: The role of psychologists in criminal trials (pp. 299–313). Stockholm: Norstedts.
Duda, R. O., &Hart, P. E. (1973).Pattern classification. New York: Wiley.
Fleming, M., &Cottrell, G. W. (1990). Categorization of faces using unsupervised feature extraction. InProceedings of the International Joint Conference on Neural Networks,2, 65–70.
Golomb, B. A., Lawrence, D. T., &Sejnowski, T. J. (1991). SEXnet: A neural network identifies sex from human faces. In D. S. Touretsky & R. Lippmann (Eds.),Advances in neural information processing systems (Vol.3, pp. 572–577). San Mateo, CA: Morgan Kaufmann.
Hopfield, J. J. (1984). Neurons with graded responses have collective computational properties like those of two-state neurons.Proceedings of the National Academy of Sciences,81, 3088–3092.
Jackson, J. E. (1991).A user’s guide to principal components analysis. New York: Wiley.
Kohonen, T. (1977).Associative memory: A system theoretic approach. Berlin: Springer-Verlag.
O’Toole, A. J., &Abdi, H. (1989). Connectionist approaches to visually based feature extraction. In G. Tiberghien (Ed.),Advances in cognitive psychology (Vol. 2, pp. 1–13). London: Wiley.
O’Toole, A. J., Abdi, H., Deffenbacher, K. A., &Bartlett, J. (1991). Classifying faces by race and sex using an autoassociative memory trained for recognition. InProceedings of the Thirteenth Annual Conference of the Cognitive Science Society (pp. 847–851). Hillsdale, NJ: Erlbaum.
O’Toole, A. J.,Abdi, H.,Deffenbacher, K. A., &Valentin, D. (in press). A low dimensional representation of faces in the higher dimensions of the space.Journal of the Optical Society of America A.
O’Toole, A. J, Deffenbacher, K. A., Abdi, H., &Bartlett, J. A. (1991). Simulating the “other-race effect” as a problem in perceptual learning.Connection Science Journal of Neural Computing, Artificial Intelligence, & Cognitive Research,3, 163–178.
O’Toole, A. J., Millward, R. B., &Anderson, J. A. (1988). A physical system approach to recognition memory for spatially transformed faces.Neural Networks,1, 179–199.
Sirovich, L., &Kirby, M. (1987). Low-dimensional procedure for the characterization of human faces.Journal of the Optical Society of America A,3, 519–524.
Author information
Authors and Affiliations
Corresponding author
Additional information
Thanks are due June Chance and Al Goldstein, for providing the faces used in the simulations, and Dominique Valentin, Candice Walker, and three anonymous reviewers, for comments on an earlier version of this manuscript.
Rights and permissions
About this article
Cite this article
O’toole, A.J., Thompson, J.L. An X Windows tool for synthesizing face images from eigenvectors. Behavior Research Methods, Instruments, & Computers 25, 41–47 (1993). https://doi.org/10.3758/BF03204447
Received:
Accepted:
Issue Date:
DOI: https://doi.org/10.3758/BF03204447