İstatistik makine öğrenimi ve değiştirilmiş adaboost sınıflayıcıyla yüz özelliklerinin tespiti

Classification modules can be used in multiple and diverse applications. The general objective of this work is the classification of facial features using statistical learning algorithms, this means being able to detect and classify the largest possible number of features that can be found on a face using only examples of images of these, without using a priori no information of the given characteristics. In the present work, detectors and classifiers of the characteristics that are considered most significant were developed, Excellent results were reported in mouth detection, with detection rates greater than 99% and errors comparable to the error from manual mouth marking. The lens sorter also obtained excellent results, with detection rates of 95% for databases with controlled environments and of the order of 90% for databases with uncontrolled environments. Beard and mustache classifiers after using the mouth detector obtained very good results, with a detection rate of over 95% in databases with uncontrolled environments.