Face Recognition Phd Thesis - .xyz

Face Recognition Phd Thesis - Sevens Report

Face Recognition is the process of identification of a person by their facial image. This technique makes it possible to use the facial images of a person to authenticate him into a secure system, for criminal identification, for passport verification,... Face recognition approaches for still images can be broadly categorized into holistic methods and feature based methods . Holistic methods use the entire raw face image as an input, whereas feature based methods extract local facial features and use their geometric and appearance properties. This paper describes how to build a simple, yet a complete face recognition system using Principal Component Analysis, a Holistic approach. This method applies linear projection to the
original image space to achieve dimensionality reduction. The system functions by projecting face images onto a feature space that spans the significant variations among known face images. The significant features known as eigenfaces do not necessarily correspond to features such as ears, eyes and noses. It provides for the ability to learn and later recognize new faces in an unsupervised manner. This method is found to be fast, relatively simple, and works well in a constrained environment.

face recognition phd thesis - DentalRedDeer

number of eigenvalues is shown in Figure 5. As seen from the figure among the 25 faces database (25 eigenfaces), 15 eigenfaces are enough for reconstruct the faces accurately. These feature or eigenpicture weights are called feature vectors.
If a multitude of face images can be reconstructed by
weighted sums of a small collection of characteristic features or eigenspaces, an efficient way to learn and recognize faces would be to build up the characteristic features by experience over time and recognize particular faces by comparing the feature weights needed to reconstruct them with the weights associated with known individuals. Each individual, therefore, would be characterized by the small set of feature or eigenspace weights needed to describe and reconstruct them – an extremely compact representation when compared with the images themselves.
This approach to face recognition involves the following initialization operations:


Face recognition phd thesis - didaonline

Face Recognition is the process of identification of a person by their facial image

number of eigenvalues is shown in Figure 5. As seen from the figure among the 25 faces database (25 eigenfaces), 15 eigenfaces are enough for reconstruct the faces accurately. These feature or eigenpicture weights are called feature vectors.
If a multitude of face images can be reconstructed by
weighted sums of a small collection of characteristic features or eigenspaces, an efficient way to learn and recognize faces would be to build up the characteristic features by experience over time and recognize particular faces by comparing the feature weights needed to reconstruct them with the weights associated with known individuals. Each individual, therefore, would be characterized by the small set of feature or eigenspace weights needed to describe and reconstruct them – an extremely compact representation when compared with the images themselves.
This approach to face recognition involves the following initialization operations: