Open AccessProceedings Article
Viewbased and modular eigenspaces for face recognition
A. Pentland
- 01 Jan 1994
pp 84-91
1.3K
TL;DR: A modular eigenspace description technique is used which incorporates salient features such as the eyes, nose and mouth, in an eigenfeature layer, which yields higher recognition rates as well as a more robust framework for face recognition.
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Abstract: We describe experiments with eigenfaces for recognition and interactive search in a large-scale face database. Accurate visual recognition is demonstrated using a database of O(10/sup 3/) faces. The problem of recognition under general viewing orientation is also examined. A view-based multiple-observer eigenspace technique is proposed for use in face recognition under variable pose. In addition, a modular eigenspace description technique is used which incorporates salient features such as the eyes, nose and mouth, in an eigenfeature layer. This modular representation yields higher recognition rates as well as a more robust framework for face recognition. An automatic feature extraction technique using feature eigentemplates is also demonstrated.<<ETX>>
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Citations
Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection
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References
Eigenfaces for Recognition
Matthew Turk,Alex Pentland +1 more
TL;DR: 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, and that is easy to implement using a neural network architecture.
6.2K
Application of the Karhunen-Loeve procedure for the characterization of human faces
Michael Kirby,Lawrence Sirovich +1 more
TL;DR: The use of natural symmetries (mirror images) in a well-defined family of patterns (human faces) is discussed within the framework of the Karhunen-Loeve expansion, which results in an extension of the data and imposes even and odd symmetry on the eigenfunctions of the covariance matrix.
2.8K
Face recognition: features versus templates
Roberto Brunelli,Tomaso Poggio +1 more
TL;DR: Two new algorithms for computer recognition of human faces, one based on the computation of a set of geometrical features, such as nose width and length, mouth position, and chin shape, and the second based on almost-gray-level template matching are presented.
2.8K
Learning and recognition of 3D objects from appearance
Hiroshi Murase,Shree K. Nayar +1 more
- 14 Jun 1993
TL;DR: The authors address the problem of automatically learning object models for recognition and pose estimation as one of matching visual appearance rather than shape and present a new compact representation of object appearance that is parameterized by pose and illumination.
297
•Book
Intelligent Robots and Computer Vision VI
David Paul Casasent,Ernest L. Hall +1 more
- 01 Jan 1987
TL;DR: These proceedings from a conference on intelligent robots and computer vision, contain papers on the following topics: pattern recognition, image processing, tactile sensors, parallel computation of image displacement fields, and object recognition.
70