The DOI matches its Crossref record.
Identification of human faces
Abstract
How well can human faces be identified by humans and by computers, using subjectively judged "feature" descriptions like long ears, wide-set eyes, etc.? Three classes of experiments are reported: 1) Gathering, analysis, and assessment of face-feature data for 255 faces. 2) Computer identification-studies. 3) Human identification-studies. A set of 22 features was evolved from an initially larger set to provide relevant, distinctive, relatively independent measures which can be judged reliably. Computer studies and a mathematical model established limits of performance of a person attempting to isolate a face from a population using feature descriptions. The model predicts that under certain conditions approximately 6 of an individual's features are required to isolate him from a population of 255. Human experiments under similar conditions showed unique identification occurred with an average of about 7 features. The model predicts that for a population of 4×106, only 14 feature-descriptions are required. These studies form a foundation for continuing research on real-time man-machine interaction for computer classification and identification of multidimensional vectors specified by noisy components.
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Citations
Cited by
Turk, M., & Pentland, A. (1991) · Journal of Cognitive Neuroscience · doi:10.1162/jocn.1991.3.1.71 · Cited by 13,733
The Representation and Matching of Pictorial Structures
Fischler, M. A., & Elschlager, R. A. (1973) · IEEE Transactions on Computers · doi:10.1109/t-c.1973.223602 · Cited by 1,322
Automatic recognition and analysis of human faces and facial expressions: a survey
Samal, A., & Iyengar, P. A. (1992) · Pattern Recognition · doi:10.1016/0031-3203(92)90007-6 · Cited by 910
Metadata
- Authors
- Goldstein, A. J., Harmon, L. D., & Lesk, A. B.
- Venue
- Proceedings of the IEEE
- Published
- 1971
- Volume
- 59
- Issue
- 5
- Pages
- 748-760
- ISSN
- 0018-9219 (ISSN-L)
- OpenAlex
- W2129733001
- Cited by
- 285
- References
- 0
- FWCI
- 1.88
- Type
- journal article
- Language
- en
- Open access
- closed
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
