Enhanced semi-supervised local Fisher discriminant analysis for face recognition |
| |
Authors: | Hong HuangAuthor Vitae Jianwei LiAuthor VitaeJiamin LiuAuthor Vitae |
| |
Affiliation: | Key Lab. on Opto-electronic Technique and Systems, Ministry of Education, Chongqing University, 400044 Chongqing, China |
| |
Abstract: | An improved manifold learning method, called enhanced semi-supervised local Fisher discriminant analysis (ESELF), for face recognition is proposed. Motivated by the fact that statistically uncorrelated and parameter-free are two desirable and promising characteristics for dimension reduction, a new difference-based optimization objective function with unlabeled samples has been designed. The proposed method preserves the manifold structure of labeled and unlabeled samples in addition to separating labeled samples in different classes from each other. The semi-supervised method has an analytic form of the globally optimal solution and it can be computed based on eigen decomposition. Experiments on synthetic data and AT&T, Yale and CMU PIE face databases are performed to test and evaluate the proposed algorithm. The experimental results and comparisons demonstrate the effectiveness of the proposed method. |
| |
Keywords: | Face recognition Dimensionality reduction Small sample size problem Semi-supervised local Fisher discriminant analysis Enhanced semi-supervised local Fisher discriminant analysis |
本文献已被 ScienceDirect 等数据库收录! |