| Summary: | 碩士 === 中原大學 === 機械工程研究所 === 95 === Owing to the urgent demand of the security system in recent year, the technology related to identity authentication using biometrics has received much attention. Biometrics such as retina, fingerprint, voiceprint and human face of every individual are unique and exclusive. Among these natures, facial feature-based recognition is one of the most straightforward and feasible ways to develop identity authentication systems. For face membership authentication systems, the challenge is how to collect numerous data for the training process, especially the unknown nonmember set. In addition, the construction of the decision hyperplane to accurately classify the unseen nonmember patterns remains difficult. This thesis proposes a new classifier which combines both one-class and binary classification strategies. The principal component analysis (PCA) is utilized to extract the most discriminating features and reduce the dimensionality of input training data first, then employ the support vector data description (SVDD) to map the member data to high dimensional feature space. Then, a spherical decision boundary is learned to exclude most nonmembers. Furthermore, support vector machines (SVM) is used to construct an optimal separating hyperplane (OSH) to distinguish the members and those few nonmembers inside the hypersphere. By using the fusion strategy, the proposed classifier outperforms others, such as SVM, ISVM (Imbalanced SVM), SVM ensemble, SVDD, when faced with nonmembers that are not included in the training. The results also indicate that the proposed classifier is able to gain better stability and generalization performance for face membership authentication.
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