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In this report, we address the problem of face verification across illumination, since it has been identified as one of the major factor degrading the performance of face recognition systems. First, a brief overview of face recognition together with its ma ...
The purpose of Face localization is to determine the coordinates of a face in a given image. It is a fundamental research area in computer vision because it serves, as a necessary first step, any face processing systems, such as automatic face recognition, ...
Detecting faces in images is a key step in numerous computer vision applications as face recognition for example. Face detection is a difficult task in image analysis because of the large face intra-class variability which is due to the important influence ...
Over the last decade, automatic facial expression analysis has become an active research area that finds potential applications in areas such as more engaging human-computer interfaces, talking heads, image retrieval and human emotion analysis. Facial expr ...
Statistical pattern recognition occupies a central place in the general context of machine learning techniques, as it provides the theoretical insights and the practical means for solving a variety of problems ranging from character recognition to face rec ...
This thesis proposes a robust Automatic Face Verification (AFV) system using Local Binary Patterns (LBP). AFV is mainly composed of two modules: Face Detection (FD) and Face Verification (FV). The purpose of FD is to determine whether there are any face in ...
The performance of face authentication systems has steadily improved over the last few years. State-of-the-art methods use the projection of the gray-scale face image into a Linear Discriminant subspace as input of a classifier such as Support Vector Machi ...
This thesis proposes a robust Automatic Face Verification (AFV) system using Local Binary Patterns (LBP). AFV is mainly composed of two modules: Face Detection (FD) and Face Verification (FV). The purpose of FD is to determine whether there are any face in ...
We compare two classifier approaches, namely classifiers based on Multi Layer Perceptrons (MLPs) and Gaussian Mixture Models (GMMs), for use in a face verification system. The comparison is carried out in terms of performance, robustness and practicability ...
The performance of face verification systems has steadily improved over the last few years. State-of-the-art methods use the projection of the gray-scale face image into a Linear Discriminant subspace as input of a classifier such as Support Vector Machine ...
