Procesamiento de imágenes para la identificación de personas como sistema de seguridad en zonas domiciliarias de la ciudad de Riobamba.

Abstract

The objective of the project is the implementation of two facial recognition algorithms for the identification of people, under real conditions in this case an address, demonstrating in a practical way the efficiency of this technology that presents many challenges due to its variants in its analysis methods. images, relevant investigations in facial identification have been explored, such as Principal Component Analysis (PCA) and Discriminant Linear Analysis (LDA) methods, mathematical processes used by the Eigenfaces and Fisherfaces algorithms, which are statistical approximation techniques for classifying data in this case faces, with minimum computational complexity. In the design and implementation of the proposed people identification system, the algorithms will be developed in Matlab, describing its main processing stages such as image acquisition, pre-processing, extractions of facial features, training or classifier, these algorithms will be evaluated of efficiency in the identification of people, used for this a base of public faces of AT & T known as ORL Database of Faces, with 400 images of different faces forgive with various expressions, in addition to this a base of faces domiciliar, with 48 images will be elaborated for system training. As a result of the evaluations under laboratory conditions they present an efficiency of over 90%, while when evaluated in real conditions, the algorithms show efficiencies ranging from 50 to 80% depending on the distances between the subject and the camera, as well as it also depends on the image quality that can be acquired with them. In this way, minimum requirements have been established for the implementation of a home surveillance system, in which favorable percentages of effectiveness will be obtained in these environments, proposing an innovation in the use of current technology in relation to video surveillance, It is recommended to carry out studies to improve techniques in facial detection and face recognition algorithms in real time.