Showing papers on "Eigenface published in 1994"
21 Jun 1994
TL;DR: In this paper, a view-based multiple-observer eigenspace technique is proposed for use in face recognition under variable pose, which incorporates salient features such as the eyes, nose and mouth, in an eigen feature layer.
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. >
2,058 citations
TL;DR: It is described how two-dimensional face images can be converted into one-dimensional sequences to allow similar techniques to be applied and how a HMM can be used to automatically segment face images and extract features that can be use for identification.
343 citations
25 Oct 1994
TL;DR: A modular eigenspace description is used which incorporates salient facial features such as the eyes, nose and mouth, in an eigenfeature layer, which yields slightly higher recognition rates as well as a more robust framework for face recognition.
Abstract: In this paper we describe experiments using eigenfaces for recognition and interactive search in the FERET face database. A recognition accuracy of 99.35% is obtained using frontal views of 155 individuals. This figure is consistent with the 95% recognition rate obtained previously on a much larger database of 7,562 `mugshots' of approximately 3,000 individuals, consisting of a mix of all age and ethnic groups. We also demonstrate that we can automatically determine head pose without significantly lowering recognition accuracy; this is accomplished by use of a view-based multiple-observer eigenspace technique. In addition, a modular eigenspace description is used which incorporates salient facial features such as the eyes, nose and mouth, in an eigenfeature layer. This modular representation yields slightly higher recognition rates as well as a more robust framework for face recognition. In addition, a robust and automatic feature detection technique using eigentemplates is demonstrated.
225 citations
05 Dec 1994
TL;DR: The authors first derive some computational feasible formula to find the eigenfaces, then investigate the relationship of mean absolute error between original face images and reconstructed images under various conditions such as face size, lighting and head orientation changes.
Abstract: Develops an approach to face recognition using eigenfaces, focusing on the effects of the eigenface used to represent a human face under several environment conditions. The authors first derive some computational feasible formula to find the eigenfaces, then investigate the relationship of mean absolute error between original face images and reconstructed images under various conditions such as face size, lighting and head orientation changes. The experimental results show that a large number of eigenfaces are not necessary to describe an individual face and only about 80 eigenfaces are sufficient for a large size set of face images. Gaussian smoothing can minimize the error under the same conditions. Finally, a face recognition system with eigenfaces and backpropagation neural network is implemented. >
88 citations
09 Oct 1994
TL;DR: A conformal mapping-based face representation technique combined with an eigenface-based method extends and improves the results obtained with other eigenfaces algorithms.
Abstract: A conformal mapping-based face representation is presented. This face representation technique combined with an eigenface-based method extends and improves the results obtained with other eigenface algorithms.
1 citations