Sistema de reconocimiento facial basado en imágenes con color (2011)
This paper develops an algorithm system to check whether the role of color can be an important attribute in facial recognition systems in two dimensions (2-D), with frontal orientation and small variations in the gestures of individuals. The first phase involves the detection and localization of the human face for which the learning algorithm uses a combination of AdaBoost and cascade classifiers to increase detection rates. In a second phase the eigenfaces approach is applied and a clasification system is implemented, to recognize and identify the subject of entry to a specific individual, using the Euclidean and Mahalanobis distance. We illustrate the results of the proposed system for both color images as gray, finding that the color information at the HSV plane can improve recognition rates when compared with the RGB plane.