Enhance face recognition accuracy by statistical regularization & dimensionality reduction
Face recognition has been a very popular research for several years with the increasing demand for a fast, accurate & robust classification engine. Several face recognition technologies with improved modifications have been developed. However, the computational burden of facial image with high d...
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| Format: | Final Year Project |
| Language: | English |
| Published: |
2015
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| Online Access: | http://hdl.handle.net/10356/65810 |
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| Institution: | Nanyang Technological University |
| Language: | English |
| Summary: | Face recognition has been a very popular research for several years with the increasing demand for a fast, accurate & robust classification engine. Several face recognition technologies with improved modifications have been developed. However, the computational burden of facial image with high dimensionality and overfitting issues due to inadequate information of real population still remains a demanding research topic. With the study of most powerful statistical analysis methods, the roles of Linear Discriminant Analysis and Principal Analysis in dimensionality reduction are sufficiently understood and investigated. Moreover, regularization methods such as eigen spectrum regularization with constant-addition and Eigen-feature Regularization and Extraction approach are also investigated. By applying the above dimensionality and statistical regularization methods, different versions of Mahalanobis classifiers are implemented using MATLAB to investigate the performance of classification accuracy on facial image databases. Based on the performance result, their corresponding quality and limitations are well compared and discussed in this project. |
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