| Summary: | 博士 === 國立中央大學 === 資訊工程學系 === 105 === This dissertation proposed a metric learning framework based on feature line embedded for person identification. In order to avoid the high computational complexity required for high dimensional features, the discriminant analysis methods was used to transform data from high dimension space to low dimension space. The discriminant analysis can reduce the feature dimension while increasing the class separation ability.
The point to line (P2L) strategy can be used to find the effective and discriminant transformation in eigenspace. So that, P2L was integrated into biased discriminant analysis algorithm to solve face verification firstly. Secondly, we used P2L in kernel space to deal the face recognition problem. Thirdly, the P2L based quadratic discriminant analysis was used to solve the person re-identification issue.
In face verification, the biased discriminant analysis focus on positive samples. The projection matrix decrease the distance between positive samples and positive center. Meanwhile, the distance between negative samples and positive center were increased. We used the concept of biased discriminant analysis to propose the P2L based BDA to solve the face verification issue.
In face recognition, since face recognition is a multi-class problem instead of two-class in face verification. Therefore, we apply the feature line embedded method preserving the local structure information in kernel space for increasing the recognition rate.
In person re-identification, the quadratic discriminant analysis based on P2L consider the data distribution and the local structure information at the same time, the performance of person re-identification could be raised.
In the experiments, we discuss the effects of various discriminant learning method on face verification, face recognition, and person re-identification. In face verification and recognition issue, the toy samples experiments showed the data distribution of discriminant analysis method. The real facial image database: Yale B, ORL, and CMU PIE face database used to evaluate the performance of face verification and recognition. The equal error rate of face verification was used to evaluate dimensionality reduction method. The recognition rate showed the effect of face recognition.
The VIPeR and QMUL GRID pedestrian dataset were used to evaluate the efficiency of person re-identification algorithm. The experimental results showed our proposed P2L strategy based quadratic discriminant analysis was the best.
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