# Multi-Layer Progressive Face Alignment by Integrating Global Match and Local Refinement
## 4 Experimental Results and Analysis
### The Performance Analysis
In general, the images on HELEN and LFPW have larger pose variations, which leads to a higher number of initialization errors than those on BioID and IMM. Figure 14: The error distribution of the different methods. To compare the performance on the different landmarks, this paper investigates the accuracy of face alignment on the internal regions and the external contour. The comparisons on the HELEN database are shown. Figure 16a,b respectively indicates the max error and the success rate of the alignment on the different landmarks. From Figure 16a, it can be seen that our method for the eyes has the lowest max error, i.e., less than 8.5. The local method on the chin has the largest max error, higher than 12. On the eyes and mouth, the local method has a lower max error than the global method. But on the cheek and chin, the global method has a lower max error. From Figure 16b, it can be seen that our method on the eyes has the highest success rate, i.e., higher than 95%. The local method on the chin has the lowest success rate, less than 86%. On the eyes and mouth, the local method has a higher success rate than the global method. But on the cheeks and chin, the global method has a higher success rate. The above results show that the local method tends to converge to the local optimum in those regions what have few texture variations; additionally, the global method can reach the global optimum on the contours. The performance can be improved by combining different methods applied to different face regions. Accordingly, our method on the eyes has the best performance. To further analyze the performance under different image conditions, this paper selected three kinds of test images, i.e., near-front, pose and occlusion, to form the difficult test set, and investigates the corresponding mean errors. As shown in Figure 17, our method has the fewest errors, and the global method has the most. The variation tendency under the three kinds of conditions remains in accordance. Our method improves performance by globally constraining the shape and carefully adjusting the local contour simultaneously. Complete texture information and reasonable shape constraint can be obtained when a face is close to the front. The near-front faces have the best performance for the three kinds of images. Our method applied to these images has the lowest mean error, less than 3.5. Partial occlusion can cause the loss of local information and the wrong convergence location based on the wrong texture. Faces under local occlusion increase the alignment difficulty and have the worst performance among the three kinds of images. Our method has the lowest mean error, less than 6. When large pose variations occur, severe self-occlusion will change the face structure because of the lack of depth information. Those landmarks on contour edges tend to be influenced by pose variation. The faces with pose have the worst performance among the three kinds of images. The error of the global alignment reaches 9.2, and the error of our method is reduced by less than 0.5. This shows that it's not enough to combine global and local information when the face has too large a pose. Three-dimensional models or multi model sets with different poses should be introduced to represent the depth information or non-linear variation, which helps face alignment across large poses. Figure 15: The initialization error of the different methods. Figure 16: The performance comparison of the different regions on LFPW database. (**a**) max error; (**b**) success rate. Figure 17: The error comparison under the different conditions.