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Face Recognition Using Principal Component Analysis Owiueyry6568391201-`=
| Content Provider | CiteSeerX |
|---|---|
| Author | Devi, Ningthoujam Sunita Hemach, K. |
| Abstract | Abstract — Face recognition is one of the most relevant applications of image analysis. It’s an efficient task (true challenge) to build an automated system with equal human ability to face recognised. Face is a complex 3D visual model and developing a computational model for face recognition is a difficult task. The paper presents a methodology for face recognition based on information theory approach of coding and decoding the face image. Proposed methodology is combination of two stages – Feature extraction using principle component analysis and recognition using the feed forward back propagation Neural Network. The proposed method has been tested on Oracle Research Laboratory (ORL) face database containing 400 images (40 classes). A recognition score for the test lot is calculated by considering almost all the variants of feature extraction. The test results gave a recognition rate of 99.50%. Keywords—Face recognition, Principal component analysis |
| File Format | |
| Access Restriction | Open |
| Subject Keyword | Face Recognition Feature Extraction Principal Component Analysis Recognition Score Face Database Relevant Application Difficult Task Abstract Face Recognition Efficient Task Face Image Equal Human Ability Automated System Computational Model Keywords Face Recognition Test Result Oracle Research Laboratory Recognition Rate Test Lot Information Theory Approach Propagation Neural Network Principle Component Analysis Image Analysis True Challenge Visual Model |
| Content Type | Text |
