Abstract
In this paper, we propose a novel classification method, called the nearest feature line (NFL), for face recognition. Any two feature points of the same class (person) are generalized by the feature line (FL) passing through the two points. The derived FL can capture more variations of face images than the original points and thus expands the capacity of the available database. The classification is based on the nearest distance from the query feature point to each FL. With a combined face database, the NFL error rate is about 43.7-65.4% of that of the standard eigenface method. Moreover, the NFL achieves the lowest error rate reported to date for the ORL face database.
Keywords
Pattern recognition (psychology)Facial recognition systemk-nearest neighbors algorithmComputer scienceFeature (linguistics)Artificial intelligenceEigenfaceFace (sociological concept)Feature extractionLine (geometry)Point (geometry)Word error rateMathematics
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Publication Info
- Year
- 1999
- Type
- article
- Volume
- 10
- Issue
- 2
- Pages
- 439-443
- Citations
- 549
- Access
- Closed
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Cite This
SZ Li,
Juwei Lu
(1999).
Face recognition using the nearest feature line method.
IEEE Transactions on Neural Networks
, 10
(2)
, 439-443.
https://doi.org/10.1109/72.750575
Identifiers
- DOI
- 10.1109/72.750575