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The impacts of facial makeup on automated face recognition system have received attention recently and studies have shown that facial cosmetics can compromise the accuracy of current face recognition techniques. Hence, there are groups of researchers endeavoring to develop the face recognition systems that are robust to facial makeup. In this work, the literatures on various techniques proposed to...
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...
Discriminant Common Vectors (DCV) is proposed to solve small sample size problem. Face recognition encounters this dilemma where number of training samples is always smaller than the data dimension. In literature, it is shown that DCV is efficient in face recognition. In this paper, DCV is enhanced for further boosting its discriminating power. This modified version is namely Discriminative Discriminant...
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...
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...
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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