## 2 Materials and Methods
### Materials and Experimental Set-Ups  
#### 2.2.1 Datasets  
We evaluated the proposed methods using four face datasets: the Cropped Extended Yale Face Database B (EYB) [34; 35], the Aberdeen dataset (ABD) [36], the pain expression subset dataset (PES) [36], and the real fabric face mask dataset (RFFMDS v.1.0) [32]. To find the best discriminative feature in partial face recognition, we specifically used the face dataset and then cropped the images to extract the eyes and eyebrows from each dataset. The cropping process for the eye images was performed using the Viola-Jones algorithm [64], while the eyebrow images were manually cropped. For the EYB, ABD, and PES datasets, the final size for the eye images was 118 \(\times\) 30 pixels, and the final size for the eyebrow images was 168 \(\times\) 22 pixels. All eye and eyebrow images were in grayscale. Figure 2 shows examples of the face images in their original form for these datasets and the cropped results for the eye and eyebrow images:  
* The Cropped Extended Yale Face Database B (EYB) [34; 35] has 38 frontal face images of 38 respondents in 65 image variations (1 ambient + 62 illuminations). The original size of each image is 168 \(\times\) 192 pixels in grayscale. Each image in the dataset has little to no variation in the location of the eyes and eyebrows. First, we evaluated 434 cropped face images taken randomly from 7 respondents in the dataset for this research. Since extremely dark images were inside the dataset, we evaluated 62 images for each respondent. We evaluated part of the EYB dataset first to observe the performance of all the proposed ideas. Then, we later re-evaluated the whole dataset using the best method that produced the highest recognition performance to observe the effect using limited data vs. larger data. Re-evaluation for the best method used 2242 images from 38 respondents. A total of 59 images was taken from each respondent. Several images were not observed because they were bad images resulting from the acquisition process.
* The Aberdeen dataset (ABD) [36] has 687 color face images from 90 respondents. Each respondent provided between 1 and 18 images. The dataset has variations in lighting and viewpoint. The original resolution of this dataset varied from 336 \(\times\) 480 to 624 \(\times\) 544 pixels. There are images with different hairstyles, outfits, and facial expressions in the ABD. We evaluated 84 face images randomly from 21 respondents, with a total of 4 images with lighting and viewpoint variations for each respondent. We also re-evaluated the whole dataset to observe the effect using limited data vs. larger data. Re-evaluations for the best method used 244 images from 61 respondents. To create a balanced dataset, we did not use the whole of the images from the 90 respondents because only 61 respondents from the dataset had the consistency of containing 4 images, while the other 29 respondents had a varying number of images per person ranging from 1 to 18 images.
* The pain expression subset dataset (PES) [36] has 84 cropped images from the pain expression dataset. The face images have a fixed location for the eyes, with 7 expressions from each of the 12 respondents. The original resolution is 181 \(\times\) 241 pixels in grayscale. The eyes and eyebrows differ in shape according to the respondent's expression. For this work, we evaluated all images in the dataset.
* The real fabric face mask dataset (RFFMDS v.1.0) [32] has 176 images from 8 respondents. A total of 22 images consisting of 2 face masks and barefaced images were gathered from each respondent. The images have varying viewpoints and head pose angles. The images are 200 \(\times\) 150 pixels in an RGB color space. This dataset was evaluated to compare the eye and eyebrow images with full-masked face images for the recognition system. For this dataset, the final size for the cropped eyes image was 49 \(\times\) 13 pixels, and the final size for the cropped eyebrows image was 67 \(\times\) 20 pixels. Both the eye and eyebrow images were in grayscale.