원문정보
초록
영어
The face recognition task involves extraction of unique features from the human face. Manifold learning methods are proposed to project the original data into a lower dimensional feature space by preserving the local neighborhood structure. PCA and LDA are used for the feature extraction and the resultant feature vectors are fused with the different fusion techniques and the proposed method yields good recognition rate with PCA Fusion of PCA and LDA features and those are classified with neural network. In general the size of the face database is too high and it needs more memory and needs more time for training so that to improve time and space complexities there is a need for dimensionality reduction. The extracted features are classified with Neural Network to improve the recognition rate.
목차
1. Introduction
2. Background Work
2.1. Existing System
2.2. Proposed System
2.3. Training
3. Testing and Comparison Study
3.1. Training and Testing of Neural Networks
3.2. System Performance
3.3. Comparison with previous FR methods
3.4. Total Error Rate
3.5. Performance Across Databases
4. Conclusions
References