Eigenfaces for Recognition
Abstract
We have developed a near-real-time computer system that can locate and track a subject's head, and then recognize the person by comparing characteristics of the face to those of known individuals. The computational approach taken in this system is motivated by both physiology and information theory, as well as by the practical requirements of near-real-time performance and accuracy. Our approach treats the face recognition problem as an intrinsically two-dimensional (2-D) recognition problem rather than requiring recovery of three-dimensional geometry, taking advantage of the fact that faces are normally upright and thus may be described by a small set of 2-D characteristic views. The system functions by projecting face images onto a feature space that spans the significant variations among known face images. The significant features are known as "eigenfaces," because they are the eigenvectors (principal components) of the set of faces; they do not necessarily correspond to features such as eyes, ears, and noses. The projection operation characterizes an individual face by a weighted sum of the eigenface features, and so to recognize a particular face it is necessary only to compare these weights to those of known individuals. Some particular advantages of our approach are that it provides for the ability to learn and later recognize new faces in an unsupervised manner, and that it is easy to implement using a neural network architecture.
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Edwards et al.'s method works with images of faces without facial hair and glasses, which are hand-labeled with the landmark points beforehand approximated with the proposed AAM.
“The method works with images of faces without facial hair and glasses, which are hand-labeled with the landmark points beforehand approximated with the proposed AAM.”
From Automatic analysis of facial expressions: the state of the art (2000) · cites this paper as [18]
The expression recognition performance of the AAM has been trained and tested on P Â PHH images of six basic emotional expressions shown by 25 subjects, and the achieved recognition rate for the six basic and neutral emotion categories was 74 percent.
“The expression recognition performance of the AAM has been trained and tested on P Â PHH images of six basic emotional expressions shown by 25 subjects.The images were chosen for limited pose and lightning variation.The achieved recognition rate for the six basic and ªneutralº emotion categories was 74 percent.”
From Automatic analysis of facial expressions: the state of the art (2000) · cites this paper as [18]
Only three of the surveyed systems deal to some extent with rigid head motions.
“Only three of the surveyed systems deal to some extent with rigid head motions ([2], [18], [30]).”
From Automatic analysis of facial expressions: the state of the art (2000) · cites this paper as [18]
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- Authors
- Turk, M., & Pentland, A.
- Affiliations
- MIT Media Lab (US)
- Venue
- Journal of Cognitive Neuroscience
- Publisher
- MIT Press
- Published
- 1991-01-01
- Volume
- 3
- Issue
- 1
- Pages
- 71-86
- ISSN
- 0898-929X (ISSN-L)
- 1530-8898
- OpenAlex
- W2138451337
- Cited by
- 13,733
- References
- 22
- Citations by year
- 2026: 100 · 2025: 182 · 2024: 175 · 2023: 208 · 2022: 259 · 2021: 310 · 2020: 350 · 2019: 404 · 2018: 488 · 2017: 523 · 2016: 606 · 2015: 688 · 2014: 784 · 2013: 761 · 2012: 729
- FWCI
- 34.31
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Journal of Cognitive Neuroscience (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Face and Expression Recognition
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Face recognition and analysis
- Life Sciences › Neuroscience › Cognitive Neuroscience › Face Recognition and Perception
- Keywords
- face recognition, eigenfaces, principal component analysis, face detection, head tracking, feature projection, unsupervised learning, neural network architecture
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