## 2 Materials and Methods  
### Materials  
The collection of standard facial datasets for benchmarking purposes was a critical component of the consistent advancements in facial expression and expression recognition. In the 1990s, different techniques and methods were introduced by various researchers to maximize facial recognition accuracy. Numerous facial recognition databases currently contain face images that differ in terms of expressions, conditions, size, occlusions, poses, number of images, and lighting. The two most popular of these databases were used in this study.  
1. The first database is the OLR, which contains a collection of face images photographed between April 1992 and April 1994 at the Olivetti Research Laboratory in Cambridge, UK. This database can be accessed via [[https://cam-orl.co.uk/facedatabase.html](https://cam-orl.co.uk/facedatabase.html)]([https://cam-orl.co.uk/facedatabase.html](https://cam-orl.co.uk/facedatabase.html)) (accessed on 10 June 2021). Accordingly, each of the 40 distinct human subjects has ten different facial photographs. The photos were taken at different times and with various facial details (no glasses/glasses) and facial appearance (non-smiling/smiling, closed eyes/open eyes). All photographs were taken against a dark homogeneous backdrop, with subjects standing frontally, upright, tolerating any rotation, and tilting up to about 20 degrees. There are some variations in the scale range, of up to 10%. Figure 1 depicts some face image samples from the OLR database. These images are grayscale image-type and have a resolution of 92 \(\times\) 112 pixels. In order to reduce computation time, we resized the selected face images in the OLR database by half of their original sizes in this work.
2. However, the second database used in this study is the Extended Yale-B database. This database contains 2432 frontal face images, each with a dimension of 192 \(\times\) 168 pixels for all the 38 human subjects. This database can be accessed at [[http://vision.ucsd.edu/leekc/ExtYaleDatabase/ExtYaleB.html](http://vision.ucsd.edu/leekc/ExtYaleDatabase/ExtYaleB.html)]([http://vision.ucsd.edu/leekc/ExtYaleDatabase/ExtYaleB.html](http://vision.ucsd.edu/leekc/ExtYaleDatabase/ExtYaleB.html)) (accessed on 10 June 2021). Furthermore, each subject has 64 photographs with varying levels of illumination. The photographs were taken under various lighting intensities and facial expressions. The intensity of lighting on these faces varies greatly across subjects, to the point where only a small portion of the face is visible in some cases. We close-cropped these face datasets with each photograph cropped to include only a look without hair or background. In addition, we resized the face images to half of their original sizes in order to reduce the computation time of the proposed model. Figure 2 shows face image samples from the Extended Yale-B database.  
Figure 1: Sample face images (**a**) different poses of two people (**b**) different poses of various people.  
Figure 2: Sample images from the Extended Yale-B database (**a**) through two poses (**b**) from all poses.  
Furthermore, a more detailed description of these two datasets can be found in Table 1, which contains the properties of these two-dimensional (2D) face datasets. The image differences are signified by (_i_) illumination, (_t_) delay time, and (_p_) pose.