| Author: | Wang, Jiannan |
| Title: | Pose-invariant face recognition |
| Advisors: | Lam, K. M. Kenneth (EIE) |
| Degree: | M.Sc. |
| Year: | 2014 |
| Subject: | Face perception -- Data processing. Human face recognition (Computer science) Hong Kong Polytechnic University -- Dissertations |
| Department: | Faculty of Engineering |
| Pages: | iv, 57 leaves : illustrations ; 30 cm |
| Language: | English |
| Abstract: | Face recognition has been widely used in security systems. The accuracy of these systems is very important for most of the applications. However, the recognition performance of most face-recognition algorithms is significantly influenced by the variations in facial appearances caused by pose variations. One of the best solutions to this problem is to generate the virtual frontal-view image from a given non-frontal-view face image under a specific pose. In this thesis, we have investigated different techniques for pose-invariant face recognition. In particular, we have employed a linear-regression-based method to handle the pose problem. In this approach, we assume that there is a linear relationship between a frontal-view face image and its corresponding non-frontal-view counterpart. We can estimate a linear mapping for the whole face and the local face regions; thus, we employ the Globally Linear Regression and the Locally Linear Regression methods, respectively, for frontal-view face reconstruction. We have compared the pose-invariant face-recognition method with Principal Component Analysis, i.e. the Eigenface method, and the FisherFace method. All of these different methods have been evaluated based on the CMU PIE database, and our experiment results show that the pose-invariant method can achieve a much better performance than the other methods when the pose variation is large. |
| Rights: | All rights reserved |
| Access: | restricted access |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| b26808237.pdf | For All Users (off-campus access for PolyU Staff & Students only) | 5.13 MB | Adobe PDF | View/Open |
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