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Semi-supervised learning approach is a fusion approach of supervised and unsupervised learning. Semi-supervised approach performs data learning from a limited number of available labelled training images along with a large pool of unlabelled data. Semi-supervised discriminant analysis (SDA) is one of the popular semi-supervised techniques. However, there is room for improvement. SDA resides in the...
Face spoofing attack is one of the recent security problems that face recognition systems are proven to be vulnerable to. The spoofing occurs when an attacker bypass the authentication scheme by presenting a copy of the face image for a valid user. Therefore, it's very easy to perform a face recognition spoofing attack with compare to other biometrics. This paper, presents a novel and efficient facial...
Face images always have significant intra-class variations due to different poses, illuminations and facial expressions. These variations trigger substantial deviation from the linearity assumption of data structure, which is essential in formulating linear dimension reduction technique. In this paper, we present a kernel based regularized graph embedding dimension reduction technique, known as kernel-based...
Locally Linear Embedding (LLE) is a popular dimension reduction technique due to its nonlinearity property. However, LLE is restricted to its unsupervised nature and “out-of-sample problem” in which suboptimal to face recognition problem. Hence, we propose a supervised and linear approximation of LLE, known as Neighborhood Preserving Discriminant Embedding (NPDE). Using the class information, NPDE...
DICOM standard is an indispensable component ofPACS, but due to non-DICOM images can be fast loaded and portable with compression, many non-DICOM medical imaging equipments are still widely used in the hospital system, which has led to existence of many non-DICOM medical images. However, the non-DICOM images are unsuitable for doctors to diagnose and study. In this paper, a low-load architecture is...
Graph Embedding (GE) along with its linearization outperforms the traditional linear dimension reduction techniques in face recognition, but there is still room for improvement on GE. This paper proposes an eigenvector weighting technique for a realization of linear GE, namely Neighbourhood Preserving Embedding (NPE) in face verification. The proposed method is called Eigenvector Weighting Function...
In order to solve the problem of "information isolated island" among e-government platform, it's urgent to build a safe, reliable, efficient, and stable platform for information sharing for resource integration of today's e-government. This paper analyses problems in the development of traditional e-government, describes the SOA (service-oriented architecture) and its related technologies...
Neighborhood Preserving Embedding (NPE) is an unsupervised linear dimensionality reduction technique which attempts to solve the ldquoout of samplerdquo problem in Locally Linear Embedding (LLE). This is done by introducing a linear transform matrix into LLE, and hence NPE can be perceived as a linear approximation to LLE. In this paper, we modify the original NPE for face recognition by embedding...
Face images are often very high-dimensional and complex. However, the actual underlying structure can be characterized by a small number of features. Hence, locally linear embedding (LLE) is proposed as a nonlinear dimension reduction technique to deal this problem. LLE learns the intrinsic manifold embedded in the high dimensional ambient space by minimizing the global reconstruction error of the...
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