2D AND 3D GLOBAL FACIAL RECOGNITION
Efficient techniques for face recognition with cross-validation in Matlab
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The research presented here is developed within the framework of automatic face recognition systems for images. This consists of processing images of people's faces using statistical and mathematical methods of feature extraction and image classification, in order to know if an individual is in a certain class, and finally find his identity. The automatic processing of a face is complicated, because there are several factors that affect it, such as the position of the face, expression, age, race, type of lighting, noise, and objects such as glasses, hat, beard, among others. The processing is ...
The research presented here is developed within the framework of automatic face recognition systems for images. This consists of processing images of people's faces using statistical and mathematical methods of feature extraction and image classification, in order to know if an individual is in a certain class, and finally find his identity. The automatic processing of a face is complicated, because there are several factors that affect it, such as the position of the face, expression, age, race, type of lighting, noise, and objects such as glasses, hat, beard, among others. The processing is performed globally, where the entire face is processed. It is known that processing images globally is faster, more practical and more reliable than feature-based images. In addition, it is known that processing images in three dimensions is more realistic and consistent than in two dimensions. The main objective of the proposed thesis was to develop an efficient face recognition technique with global features, and with images in three dimensions. To this end, the most efficient algorithms were selected
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