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Practical face recognition systems are sometimes confronted with low-resolution face images. Traditional two-step methods solve this problem through employing super-resolution (SR). However, these me...
This paper proposes a novel nonlinear discriminant analysis method named by Kernerlized Maximum Average Margin Criterion (KMAMC), which has combined the idea of Support Vector Machine with the Kernel ...
Illumination variation is one of the bottlenecks of face recognition systems. In the past few years, many approaches to coping with illumination variations have been proposed which can be categorized...
In this paper, we present the rarely concerned curse of mis-alignment problem in face recognition, and propose a novel mis-alignment learning solution. Mis-alignment problem is firstly empirically ...
This paper presents support vector machines (SVM)for few samples-based face recognition with two-layer artificially generated virtual training data. The few samples cannot express all the conditions ...
This paper presents a novel face recognition method based on cascade Linear Discriminant Analysis (LDA) of the component-based face representation. In the proposed method, a face image is represented ...
Face recognition has attracted much attention in the past decades for its wide potential applications. Much progress has been made in the past few years. However, specialized evaluation of the state-...
Illumination Normalization for Robust Face Recognition against Varying Lighting Conditions.
Pose Normalization for Robust Face Recognition Based on Statistical Affine Transformation.

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