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Cited by 80Face and Expression RecognitionAdvanced Image and Video Retrieval TechniquesFace recognition and analysis

Distortion invariant object recognition by matching hierarchically labeled graphs

Buhmann, Lange, von der Malsburg·1989·International Joint Conference on Neural Networks
DOI 10.1109/ijcnn.1989.118574
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Abstract

A graph-matching process of object recognition is proposed. It is applied to face recognition. Gray-level images are represented by a resolution hierarchy of local Gabor components, which are all scaled and rotated versions of each other. The components centered on one image point form a Gabor jet. A single jet provides a distortion-insensitive local representation of part of an image. Object recognition is achieved by matching image point jets to jets in stored prototype patterns. For a selected image jet the best matches are determined, under a constraint preserving spatial arrangement. The procedure amounts to labeled graph matching, with Gabor jets forming labels to nodes and topology determining links. A contrast-insensitive similarity measure provides for invariance with respect to lighting conditions. The authors have formulated the matching procedure as an optimization task solved by diffusion of match points. This diffusion is controlled by a potential determined by jet similarity and the topology-preserving constraint. The algorithm implements a neural network architecture.>

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Crossref · 10.1109/ijcnn.1989.118574

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12 of 12 references verified0 citation errors · 0 not checkable

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Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • For most work in automatic facial expression analysis, the conditions under which an image is obtained are controlled.

    “For most of the work in automatic facial expression analysis, the conditions under which an image is obtained are controlled.”

    From Automatic analysis of facial expressions: the state of the art (2000) · cites this paper as [59]

  • In most work in automatic facial expression analysis, the camera is either mounted on a helmetlike device worn by the subject or placed so that the image has the face in frontal view.

    “The camera is either mounted on a helmetlike device worn by the subject (e.g., [62], [59]) or placed in such a way that the image has the face in frontal view.”

    From Automatic analysis of facial expressions: the state of the art (2000) · cites this paper as [59]

  • Otsuka and Ohya's method is not sensitive to unilateral appearance changes of the left eye.

    “As a consequence, their method is not sensitive to unilateral appearance changes of the left eye.”

    From Automatic analysis of facial expressions: the state of the art (2000) · cites this paper as [59]

  • Only two of the surveyed systems process images acquired by a mounted camera.

    “Only two of the surveyed systems process images acquired by a mounted camera ( [59], [62])”

    From Automatic analysis of facial expressions: the state of the art (2000) · cites this paper as [59]

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Authors
Buhmann, Lange, & von der Malsburg
Affiliations
University of Southern California (US)
Venue
International Joint Conference on Neural Networks
Publisher
IEEE
Published
1989
Pages
155-159 vol.1
ISSN
2161-4407 (ISSN-L)
DOI
10.1109/ijcnn.1989.118574
OpenAlex
W2008130267
Cited by
80
References
12
Citations by year
2023: 1 · 2022: 1 · 2017: 1 · 2015: 2 · 2014: 1 · 2013: 1
FWCI
13.51
Type
conference paper
Language
en
Open access
closed
Locations
International Joint Conference on Neural Networks (IJCNN) (publishedVersion)
Funders
Air Force Office of Scientific Research
Topics
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Face and Expression Recognition
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Image and Video Retrieval Techniques
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Face recognition and analysis
Keywords
object recognition, face recognition, graph matching, distortion invariance, illumination invariance, neural network architecture, similarity measures

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