**4. Experiments and analysis**

In order to investigate the reliability of LCGS algorithm, we use the AR physical occlusion database and the ORL simulation occlusion database to conduct the experiments. The AR face database consists of 126 people, a total of 3276 images, 26 pictures per person. The 26 pictures are collected in two different time periods, including changes in light, occlusion, and so on. The shelter is divided into scarves and sunglasses. The ORL Face Database was created by the University of Cambridge AT &T Lab, which contains 40 people, 10 images per person, and a total of 400 face images. We use a baboon image to randomly block the original image for the occlusion simulation. Some examples of AR database and ORL database are shown in **Figures 6** and **7**.

**Figure 6.** Examples of AR face database.

person, a total of 300 images. The shelter in test data set 2 is sunglasses, three images per person, a total of 300 images. The shelter in test set 3 is a mixture of scarves and sunglasses, each with six images, a total of 300 images. The results of the recognition rates are shown in **Table 1**. Through **Table 1**, we can see that recognition rate achieved by LCGS algorithm is higher than other algorithms for all these three test data set. In particular, when the shelter of the test set is a scarf, the recognition rates of the LGS, SLGS, and MOW-SLGS algorithms are 83.67, 88.47, and 89.67%. Moreover, the recognition rate of the LCGS algorithm is 91.27%, which well demonstrates the advantages of our proposed method in the occluded images with a scarf. We also find that the recognition rate for test set 1 is the lowest among the three test data sets. This is due to the occlusion caused by the scarf almost accounted for 20% of the image. It has a great

**Test set LGS SLGS MOW-SLGS LCGS** Test set 1 (scarf) 0.8367 0.8847 0.8967 0.9127 Test set 2 (sunglasses) 0.8970 0.9413 0.9380 0.9533 Test set 3 (scarf + sunglasses) 0.8620 0.9147 0.9203 0.9295

Face Recognition with Facial Occlusion Based on Local Cycle Graph Structure Operator

http://dx.doi.org/10.5772/intechopen.78597

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We performed an analog occlusion experiment on the ORL face database, and we chose LGS, SLGS, and MOW-SLGS to compare our proposed algorithm, LCGS. We used a baboon picture to block the original face picture randomly. We set the occlusion area as 10%, 20%, 30%, 40%, and 50% of the original image. We set up three training sets. The first group selected six images per person, a total of 240 images. The second group selects seven images per person, a total of 280 images. The third group selects eight images per person, a total of 320 images. Corresponding, the test set is also divided into three groups. The test set 1 is four images per person, a total of 160 images. The test set 2 is three images per person, a total of 120 images. The test set 3 is two images per person, a total of 80 images. The results are shown in **Table 2**,

**Occlusion area (%) LGS SLGS MOW-SLGS LCGS** 0.5425 0.5731 0.6375 0.6713 0.5156 0.5531 0.6188 0.6275 0.4587 0.5300 0.5587 0.6019 0.3625 0.4450 0.4731 0.5063 0.2863 0.3606 0.3775 0.4044

**Table 2.** Recognition rates of different algorithms on test set 1 of the ORL database.

effect on the feature extraction of the whole image.

**Table 1.** Recognition rates of different algorithms on the AR database.

*4.3.2. Experimental results on ORL face database*

**Figure 8**, and **Table 3**.

**Figure 7.** Examples of ORL face database.
