**5. Conclusion**

From **Table 2**, **Figure 8**, and **Table 3**, we can clearly see that the recognition rate of the LCGS algorithm is higher than that of the conventional algorithms. In the test set 1, the recognition rates of the LGS, SLGS, and MOW-SLGS algorithms are 45.87%, 53.00% and 55.87%, respectively, when the occlusion area is 30%, and the recognition rate of the LCGS algorithm is 60.19%.

**Occlusion area (%) LGS SLGS MOW-SLGS LCGS** 0.5700 0.5988 0.6975 0.7013 0.5362 0.5838 0.6663 0.6738 0.4900 0.5688 0.5925 0.6200 0.4025 0.4763 0.5275 0.5463 0.2888 0.4113 0.3825 0.4188

In order to further verify the accuracy of the algorithm, we also conducted 10-fold cross-validation. We chose the ORL database and randomly masked only one piece of data for testing. The compari-

Through the experimental results, as shown in **Table 4**, we can see that LCGS algorithm performs better than other algorithms. The recognition rates of the LGS, SLGS, and MOW-SLGS algorithms are 49.00%, 53.00%, and 55.25%, respectively, when the occlusion area is 40%

During the experiments, we compared the processing time for the same image required by the LGS, SLGS, MOW-SLGS, and our proposed LCGS algorithm. The result is shown in **Table 5**.

son algorithms are LGS, SLGS, and MOW-SLGS. The result is shown in **Table 4**.

while the recognition rate of the LCGS algorithm is 61.75%.

**Table 3.** Recognition rates of different algorithms on test set 3 of the ORL database.

**Figure 8.** Recognition rates of different algorithms on test set 2 of the ORL database.

*4.3.3. 10-fold cross-validation*

64 Machine Learning and Biometrics

*4.3.4. Comparison of the processing time*

In this chapter, we proposed a LCGS algorithm and applied it to face recognition with occlusion. LCGS makes full use of the texture features of the surrounding pixels in the 3 × 3 neighborhood. It makes up for the shortcomings of LGS and MOW-SLGS algorithm where the information around the target pixel is not sufficient. The characteristics of the target pixel value using LCGS algorithm is easier to recognize. Therefore, it improves the recognition rates with occlusion. Through the experiments on the AR and ORL database, we demonstrated that the LCGS algorithm is superior to the traditional algorithm in the recognition rate of face images with occlusion, and the time consumption is lower than the MOW-SLGS algorithm.
