Semi-supervised nearest neighbor discriminant analysis using local mean for face recognition

Authors:
Caikou Chen;Pu Huang;Jingyu Yang
Affiliations:
Information Engineering College, Yangzhou University, Yangzhou, China;Information Engineering College, Yangzhou University, Yangzhou, China;School of Computer Science and Technology, Nanjing University of Science and Technology, Nanjing, China
Venue:
AICI'10 Proceedings of the 2010 international conference on Artificial intelligence and computational intelligence: Part I
Year:
2010

Citing 5
Cited 0

Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection

IEEE Transactions on Pattern Analysis and Machine Intelligence
Face Recognition Using Laplacianfaces

IEEE Transactions on Pattern Analysis and Machine Intelligence
Graph Embedding and Extensions: A General Framework for Dimensionality Reduction

IEEE Transactions on Pattern Analysis and Machine Intelligence
Globally Maximizing, Locally Minimizing: Unsupervised Discriminant Projection with Applications to Face and Palm Biometrics

IEEE Transactions on Pattern Analysis and Machine Intelligence
Eigenfaces for recognition

Journal of Cognitive Neuroscience

Quantified Score

Hi-index	0.00

Visualization

Abstract

Feature extraction is the key problem of face recognition. In this paper, we propose a new feature extraction method, called semi-supervised local mean-based discriminant analysis (SLMNND). SLMNND aims to find a set of projection vectors which respect the discriminant structure inferred from the labeled data points, as well as the intrinsic geometrical structure inferred from both labeled and unlabeled data points. Experiments on the famous ORL and AR face image databases demonstrate the effectiveness of our method.