In this chapter, the performance of different texture descriptor algorithms used in face feature extraction tasks are analyzed. These commonly used algorithms to extract texture characteristics from images, with quite good results in this task, are also expected to provide fairly good results when used to characterize the face in an image. To perform the testing task, an AR face database, which is a standard database that contains images of 120 people, was used, including 70 images with different facial expressions and 30 with sunglasses, and all of them with different illumination intensity. To train the recognition system from one to seven images were used for each person. Different classifiers like Euclidean distance, cosine distance, and support vector machine (SVM) were also used, and the results obtained were higher than 98% for classification, achieving a good performance in verification task. This chapter was also compared with other schemes, showing the effectiveness of all of them.
Part of the book: From Natural to Artificial Intelligence