# An Efficient Multiscale Scheme Using Local Zernike Moments for Face Recognition
## 4 Experimental Results
### Discussion
#### 4.4.2 Parameters  
In this study, some parameter values used in the experiments performed on FERET, LFW, and SCface datasets were different. These are as follows:  
* The number of scales used for extracting multi-scale features and the sizes of face images
* Moment degrees and filter sizes used in LZM transformations.  
The parameter values used for each dataset are given in Table 5. These values are different because the resolutions of the images presented in the datasets are very different from each other. There are too many low-resolution images in SCface, since the images of the moving persons are recorded at different distances with surveillance cameras. In order to ensure that the image distortions on the face images are not excessive, the face images were used in only three different sizes in the experiments performed with this dataset. In FERET and LFW, the quality of the images is better and therefore five different scales were used to reveal the details in the faces better.  
An image can be reconstructed using its own Zernike moments. As the moment degree used increases while the Zernike moments are calculated, the error level of the reconstructed image decreases [80]. In other words, the details of an image can be better encoded by increasing the moment degree. In the experiments performed with FERET, LFW, and SCface-Vis (DayTime protocol), the moment degree was four in both transformations. However, in the experiments performed with SCface-IR (NightTime protocol), the moment degree was one in the first transformation and four in the second transformation. The reason for this situation is that there are fewer details about the faces in the images in SCface-IR. In the first transformation, when low-resolution face images were encoded by taking the moment degree small, intra-class similarities and between-class differences were better preserved. When the moment degree was selected larger, the similarities between the classes increased because there was less distinguishing information to be encoded in the images. While five was used as the filter size in both LZM transformations for FERET, seven was used for other datasets. FERET consists of images obtained from controlled environments, and the images in this dataset have better resolution than the other datasets used in this study. Hence, the area to be locally encoded was kept narrower, so that the details of the face images were more discriminatively encoded. Since the other datasets consist of images from uncontrolled environments, the details in the images have less distinguishing information. Therefore, larger regions were encoded using a larger filter size for these datasets.  
In Table 5, we provide the execution time for each different setting to extract features from a face image. When calculating execution times, we did not include the time spent on dimensionality reduction operations in order to clearly see the effect of the parameter values used on execution time. It is possible to reduce the given feature extraction times with a careful implementation and using parallel programming techniques. We have implemented MS-LZM using C++ with OpenCV library, and performed the experiments on a PC with Intel Core i7-3930K CPU and 40 GB RAM.  
With the LZM transformation, the desired number of filters can be obtained according to the selected moment degree and number of repetitions. Since the characteristics of these filters are different from each other, different features of face images are extracted with each filter. In this study, all the filters obtained according to the used moment degree were used to construct the feature vectors. Instead of using all filters, using a suitable optimization method, a subset of the filters can be selected that enables the extraction of more distinctive features, and thus better results are likely to be obtained.