# An Efficient Multiscale Scheme Using Local Zernike Moments for Face Recognition
## 4 Experimental Results
### Experiments on FERET  
FERET [54] includes a gallery set called Fa and four different probe sets called Fb, Fc, Dup1, and Dup2. There are 1196 different people in Fa, and each person has only one image. In the probe sets, there are 1195, 194, 722, and 234 images, respectively. The images in Fb have different facial expressions and the same lighting conditions as those in Fa, and these two sets are composed of images which were recorded with the same camera on the same day. Likewise, Fc and Fa sets also consist of images which were recorded on the same day. However, these images have different lighting conditions. The images in Dup1 were recorded in the same year as the images in Fa, but on different days. The last probe set Dup2 consists of images which were recorded in different years from the images in Fa. Dup1 and Dup2 sets were recorded using different cameras. These sets have facial expressions, occlusion, scale, and lighting conditions than Fa. In Figure 5, some sample images taken from FERET are shown. We followed the standard protocol of FERET in the experiments and reported the identification performance with Rank 1 recognition rate. The characteristics of the filters used in the LZM transformation are quite different from each other, as shown in Figure 1. Therefore, different features of face images are extracted with each filter. In order to understand the effects of these filters on the recognition performance, we computed features separately with the filters obtained for each different \((n,m)\) pair. Using these features, the results achieved for Fb, Fc, Dup1, and Dup2 are shown graphically in Figure 6.  
When these results were calculated, facial images were used in only one scale and resized so that the distance between the eyes was 80 pixels. Feature vectors were constructed around the 21 facial points shown in Figure 4. Dimensionality reduction was not performed on the vectors and the L1-norm was chosen as the distance criterion, since we did not have eigenvalues to use the similarity function given in (13). In the graphs given in Figure 6, the results computed using the filters up to \(n=6\) are shown. The blue markings on these graphs show the recognition rates achieved using each filter separately. The red markings (cumulative results) show the rates achieved using the concatenation of the feature vectors which are computed using filters up to \(n=6\). When these results were examined, it was seen that the performance of each filter was quite different, especially for Dup1 and Dup2. The sixth filter corresponding to \(n=4\) and \(|m|=4\) pairs had the lowest success for Fb, Dup1, and Dup2. When cumulative results were examined, it could also be seen that the filters with low success had  
Figure 5: Sample face images from the FERET dataset. Images were aligned using a similarity transformation and cropped. no negative effect on the result. The second and fifth filters were the most successful. In particular, the success achieved with the fifth filter was the same as the cumulative success. The LZM transformation was applied twice in succession in this study. Therefore, in order to analyze the changes in performance when filters were used repeatedly, the graphs given in Figure 7 were generated for Dup1 and Dup2. Here, the results obtained with filters up to \(n=4\) in the first transform and up to \(n=5\) in the second transform are shown. In order to make a better comparison, the results given in Figure 6 are also shown in Figure 7. The best results for Dup1 and Dup2 (the cumulative filter results when \(n=6\) and \(|m|=6\)) from Figure 6 are given by the green horizontal lines. The yellow markings show the performance per filter from Figure 6. These results are shown in accordance with the values of \(n2\) and \(|m2|\), and they are repeated for each different \(n1\) and \(|m1|\) pair. Comparing blue and yellow markings, it can be seen that better results could be obtained for the majority of filters if the filters were applied in succession.