# Multi-Sensor Face Registration Based on Global and Local Structures
## 4 Experimental Results
### Registration on Real Face Images with the UTK-IRIS Database
#### 4.1.2 Quantitative Evaluation  
In order to give a quantitative comparison of our FR-GLS and other typical methods, such as CPD [19], RGF [20] and SMM [25], we further utilize several visible and infrared image pairs of the four individuals in Figure 4, and manually selected several landmarks and their correspondences (about 20 pairs) in each visible and infrared image pairs as ground truth. For example, the landmarks involve the salient features like the cheeks, eyes, nose, mouth, eyebrows, ears and glasses, which are highly distinguished. Referring to the metric used in [20; 21; 35], the recall rate is proposed to be a qualitative indicator for evaluating the registration results on all landmarks set pairs of an individual. Here the recall rate, or true positive rate, is defined as the proportion of the true positive correspondences between the landmark pairs to the ground truth correspondences, and a true positive correspondence is counted when the pair falls within a given accuracy threshold of Euclidean distance. Here the Euclidean distance can be defined as 2-norm between a landmark in the aligned feature point set of visible images and the corresponding landmark in the feature point set of infrared images. The recall curves plot the recall rates under different threshold values.  
Figure 5 shows the recall rates against different threshold values using the registration methods of CPD, SMM, RGF and FR-GLS, considering four individuals. Each individual contains about 100 hundred-image pairs corresponding to every spectrum. By the definition of recall, we can see that the FR-GLS and RGF methods for the global and local structures are significantly superior to the CPD and SMM methods for the global structures only based on finite mixture models. Moreover, the curves of our FR-GLS method are consistently above the curves of the RGF method for different accuracy thresholds and individuals, which could be attributable to the combination of the robust SMM and IDSC local feature descriptor.  
Figure 4: Registration and fusion results of our method on four typical unregistered visible/infrared image pairs in the database of UTK-IRIS. (**a**) [PERSON]; (**b**) [PERSON]; (**c**) [PERSON]; (**d**) [PERSON].  
Figure 5: Quantitative comparisons of multispectral face image pairs with four individuals in Figure 4. (**a**) [PERSON]; (**b**) [PERSON]; (**c**) [PERSON]; (**d**) [PERSON].  
The average registration errors of each individual for different registration methods are summarized in Table 1. We again see that the FR-GLS and RGF methods with the global and local structures significantly outperform the CPD and SMM methods just with finite mixture models.  
Moreover, our FR-GLS method can achieve consistently better performance compared to the RGF method. The average registration errors of this RGF method are about 2.40, 2.24, 2.24 and 1.92 pixels on the four individuals, respectively. By contrast, the average registration errors of our FR-GLS method are reduced to about 2.25, 2.06, 1.99 and 1.78 pixels, respectively. In addition, The SMM method with SMM has a slightly better performance than the CPD method with GMM, where the average registration errors are 2.88, 2.40, 2.56 and 2.13 pixels, respectively. Whereas, the average registration errors of the CPD method are up to 3.04, 2.58, 2.73 and 2.35 pixels, respectively. It can quantitatively verify the heavy-tailed property of SMM.