# Multi-Sensor Face Registration Based on Global and Local Structures
## 4 Experimental Results
### Registration on Real Face Images of Self-Built Multi-Spectral Database
#### 4.2.1 Qualitative Comparison  
Referring to the qualitative evaluation in Section 4.1, Figure 7 shows an intuitive impression of registration and fusion results on six typical frame pairs in the self-built multi-spectral database.  
As shown in the Figure 7 above. Firstly, several original visible images and thermal infrared images of six individuals are given in Figure 7a,b, respectively, and those face images involve different poses and illumination changes. In addition, the image pairs in the bright light are shown in the first three rows, while the image pairs in the dark condition are shown in the last three rows with different poses. Then, Figure 7c shows the discrete point set of edge maps, which are extracted by the canny edge detector. For distinguishing each spectral image, we also use the blue point set to denote the edge  
Figure 7: Registration and fusion results of our method considering six typical unregistered visible and infrared image pairs in the self-built multi-spectral face datasets. (**a**) Visible image; (**b**) Thermal infrared image; (**c**) Canny edge maps; (**d**) Superimposed checkerboard pattern of visible and infrared images; (**e**) Fusion results.  
map of the visible image, and the red point set corresponds to the edge map of the thermal infrared image. We can see that contour point sets of the face images in the edge map pairs are significantly different for the thermal infrared and visible images. Those discrepancies will result in a lot of outlier and noise for the edge maps alignment, especially in the textured regions of hairs and clothes. Hence, it is hard to match the two edge map pairs accurately.  
Furthermore, note that we just focus on the registration results of the face region, the redundant information, such as the edge points of clothes, which is not related to face detection, might be ignored during the matching. Subsequently, the visible image is aligned to the thermal infrared image by our FR-GLS method, and the registration performance is demonstrated by checkerboard images in Figure 7d, where the aligned visible image is superimposed to the corresponding thermal infrared image. In addition to the uninterested regions of hairs and clothes, we can see from Figure 7d that the seams between two grids in the checkerboard images are natural, and this can demonstrate the feasibility and effectiveness of the proposed registration method qualitatively. Finally, for further verification, Figure 7e shows the fusion results of the visible and thermal infrared images by our GF-GP method. Compared to the original thermal infrared images in the second column (Figure 7b), we can see that the fused images in the last column (Figure 7e) seem to be sharpened, which contain both the apparent details of the visible image and the thermal radiation information of the infrared image. Thus, it will be beneficial for follow-up face detection and recognition in case of illumination changes, e.g., the outdoor environment illumination changed from bright to dark.