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
Affiliated Institutions
Related Publications
Eigenfaces vs. Fisherfaces: recognition using class specific linear projection
We develop a face recognition algorithm which is insensitive to large variation in lighting direction and facial expression. Taking a pattern classification approach, we conside...
Efficient Pattern Recognition Using a New Transformation Distance
Memory-based classification algorithms such as radial basis functions or K-nearest neighbors typically rely on simple distances (Euclidean, dot product...), which are not partic...
Face recognition using Laplacianfaces
We propose an appearance-based face recognition method called the Laplacianface approach. By using Locality Preserving Projections (LPP), the face images are mapped into a face ...
SVM-KNN: Discriminative Nearest Neighbor Classification for Visual Category Recognition
We consider visual category recognition in the framework of measuring similarities, or equivalently perceptual distances, to prototype examples of categories. This approach is q...
Matching shapes
We present a novel approach to measuring similarity between shapes and exploit it for object recognition. In our framework, the measurement of similarity is preceded by (1) solv...
Publication Info
- Year
- 1999
- Type
- article
- Volume
- 10
- Issue
- 2
- Pages
- 439-443
- Citations
- 549
- Access
- Closed
External Links
Social Impact
Altmetric
PlumX Metrics
Social media, news, blog, policy document mentions
Citation Metrics
549
OpenAlex
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