3. Evaluation results

In this chapter, the system was evaluated using the identification and verification tasks, wherein identification of the system is required to determine the identity of the person under analysis by comparing the facial characteristics stored in a database with the face characteristics extracted and the verification task, where the system must define if the identity corresponds with the person she/he claims to be [4]. In both tests, the results are compared with another algorithm like the eigenphase [11], Laplacianfaces [29], Fisherfaces, and eigenfaces [28], all of them used in the classification task with the SVM and k-means with Euclidean and cosine distance, the AR Face Database [35] was used for all tests.

The AR database was expanded with four additional images for each one in the original AR database. These images are shown in Figure 5, where Figure 5a is the original image and Figure 5b–e show the resulting images of the illumination variations.

After the expansion, the AR database has 12,000 face images in total, each person has 100 images and the database has 120 persons, where 65 are males and 55 females. The database was divided into two sets, the AR(A) which has 70 images with illumination and expression

Figure 5. Effects of illumination transformation applied to form the extended AR database.

changes and the AR(B) which has 30 images with partial occlusion using sunglasses and also illumination changes. Figure 6 shows some examples of these images.

In real-world applications, the number of training images and recognition accuracy are strongly related; as more images are used for training, the recognition accuracy improves. However, in real applications the number of training images is limited. Figure 7 shows the performance of hLBPI (Figure 7a) and WBP (Figure 7b) with different number of training images using three different classifiers.
