## 1 Introduction
(2019) supported that the eyes, eyebrows, mouth, chin, and nose can be discriminative facial features for distinguishing the human race. [PERSON] et al. (2019) utilized the eye and eyebrow regions using partial features for facial expression recognition. They achieved similar results compared to using full-face information. [PERSON] et al. (2019) also investigated the eyebrow and mouth areas for facial expression recognition. As early as 2003, [PERSON] et al. (2019) evaluated the importance of eyebrows in face recognition. Their findings recommend that the eyebrows are a significant feature in face recognition. The three possible reasons are as follows. First, the eyebrows carry emotions and other nonverbal signals. Second, the eyebrows may assist as a solid feature due to high contrast. They are robust against image degradation and illumination changes. Third, the eyebrows are considered a consistent feature because of the incredible variety among individuals and reliability across short periods of time (weeks or months). Various works analyzed eyebrow images for recognition. In their work, [PERSON] and [PERSON] (2019) extracted string features. Hidden Markov models were employed in ([PERSON] et al., 2019). [PERSON] and [PERSON] in (2019) proposed the novel features of a three-patch local binary pattern and Weber local descriptor. [PERSON] et al. (2019) designed fast template matching and a Fourier spectrum distance for an automatic human eyebrow recognition system. These previous works have shown that eyebrows have the potential to be future biometric traits. Although they were highly distinctive, few works have investigated the value of adding a curvature-based method for the eyes and eyebrows to produce in-depth characteristics. In ([PERSON] et al., 2019), curvature was added and combined with a gray level co-occurrence matrix (GLCM) and tested against a masked face recognition system. Although the added curvature did not improve the recognition's performance, it successfully simplified the number of properties in the GLCM and the recognition's running time. In ([PERSON] et al., 2019), the periocular images were observed against the wavelet best basis (WBB) method. They were analyzed using wavelet characteristics and variations in geometry transformations and noisy images. Although the WBB improved the accuracy performance by 5-12%, the highest accuracy (86.33%) had not achieved satisfactory results. In this work, we focused our observation on using the eye and eyebrow regions from face images datasets (cropped extended Yale B dataset ([PERSON] et al., 2019; [PERSON] et al., 2019), Aberdeen dataset ([PERSON] et al., 2019), pain expression subset dataset ([PERSON] et al., 2019), and real fabric face mask dataset ([PERSON] et al., 2019)) to determine which feature was the most discriminative for a partial face recognition system. The eyes and eyebrows on the face have shapes that have details such as corners, lines, edges, and colors. Moreover, the eyebrows are located next to the forehead, therefore increasing their visibility [37]. A curvature-based method offers character to these details due to its ability to measure how curved a surface is. Generally, curvature-based methods need 3D images (where the third dimension offers the height value of the image data) [38; 39; 40], but in this research, we propose using 2D images and transforming them into three-dimensional data, where the third dimension contains the intensity value of the image. In addition, these periocular features may not offer distinctive details on the coarse scales but may be revealed on the finer scales. These finer scales can be obtained with the multiscale analysis method. Thus, we proposed an idea: combining the multiscale analysis methods (i.e., scale space (SS) and discrete wavelet transform (DWT)) with a curvature-based method. This list contains the highlights and contributions of this work:  
* We compare the eyes and eyebrows to find the most discriminative facial feature for a partial face recognition system.