Personalized Face Verification System Based on Cluster-Dependent LDA Subspace
2003
Hochschulschrift
Zugriff:
91
Recently, person authentication becomes more and more important as technology advances. How to build a safe and convenient identity verification system is a hot research topic in academia and business. In this thesis, we introduce a personalized face verification system based on cluster dependent LDA subspace. The training of the system can be divided into three parts: the initial training, on-site training, and on-site evaluation. In the initial training, we select some human face images of our database as representative face images. The images can be clustered by using K-means clustering method. For on-site training, the client must give some face images for on-site training. We can assign the client to the closest cluster. To separate the client from other representative people in the cluster, we will adopt LDA method to the LDA subspace. At last, we use information of the client and impostors to adjust the threshold. In the part of system operation and on-line training, we can manually input the password when we cannot verified by the system. The system can get more training images to retain the LDA subspace and threshold. We also compare three different matching scores. The experimental results show our method outperforms the traditional LDA method。
Titel: |
Personalized Face Verification System Based on Cluster-Dependent LDA Subspace
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Autor/in / Beteiligte Person: | Liu, Hsien-Chang ; 劉憲璋 |
Link: | |
Veröffentlichung: | 2003 |
Medientyp: | Hochschulschrift |
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