Face Recognition Based on Texture Descriptors

Abstract

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.

Evaluating face recognition with different texture descriptions and convolution neural network

“…The computational efficiency of LBP, in addition, since it provides good support especially when the images have changes in the level of intensity, made several types of LBP used for face recognition purposes [20]: holistic LBP histogram (hLBPH), the spatially enhanced LBP histogram (eLBPH), holistic LBP Image algorithm (hLBPI) and decimated image window binary pattern (WBP)). All of these algorithms depended on the original LBP algorithm.…”