# Person Identification and Gender Classification Based on Vision Transformers for Periocular Images
## 2 Related Works  
Among the first researchers in this area were [PERSON] et al. [7], who mentioned periocular biometrics categorized under standalone biometric peculiarities. They have emphasized extracting global and local information and used surface-texture and limited-neighborhood operators to obtain a feature matrix for the purpose of categorization. Position variations, masking of the iris, template aging, face occlusion, and eye regions were the utmost discerning features, with pose deviations, template aging, occluded iris, face occlusion, and eye areas being distinguished factors that cause overall accuracy to deteriorate [7; 8]. The authors designed a user interface to show human subjects who were shown a pair of ocular images in an experiment. The features were ranked, starting with the most beneficial and ending with the least beneficial, as (i) eye shape, (ii) eyelids, (iii) eyelashes, (iv) eyebrows, (v) tear duct, (vi) skin, and (vii) outer corner. The testers were provided with multiple periocular images and were tasked with ascertaining the validity of the same [9]. When shown images taken in the visible and NIR spectra, participants were told to identify periocular pictures in pairs, whether they belong to the same or a different individual [10]. Projections-based methods like eigenvector-based PCA, derivatives, and discriminant function analysis are also investigated [11]. When the performance of iris identification algorithms deteriorates, the periocular area has been demonstrated to give exceptional recognition rates in complicated picture acquisition settings considering inadequate image blur, iris segmentation, specular reflections, and occlusions caused by eyelids and eyelashes. The findings suggest that with the ocular region's size, system accuracy does not necessarily improve [12]. A periocular image is broken down into several boxlike areas, improving the ability to differentiate the matched biometric pattern. It is achieved by an unsupervised patch selection procedure [13]. A significant space of the periocular neighborhood is viable for obstructed images. The joint representation of periocular texture and structure effectively expresses and poses invariant representation [14]. For less restrictive periocular matching outcomes, a periocular identification technique based on SCNN (semantics-assisted convolutional neural networks) can outperform alternatives in accuracy and matching time [15]. It uses explicit semantic information to extract natural periocular properties automatically. Another study found five local textures or geometrical features based on periocular area landmarks to possess discriminating racial information. Adaboost training combined these features into a prominent feature that enhanced the average accuracy rate [16]. A multi-scale technique was retrieved in which characteristics were from the face, periocular, shoulders, and head area. They were then combined in a two-stage process with a pair of classifiers, resulting in increased efficiency [17]. A merging model technique outperforms the outcome of employing a particular CNN model for the right and left pictures in terms of low-resolution deterioration and blockage. The authors developed two separate CNN models for each eye, one for the left and one for the right, and then combined them to produce a new CNN model [18]. Instead of iris texture, gender-related information was mainly found in the periocular region. Linear SVM and CNN were compared with handcrafted and deep features [19]. Another work implemented periocular recognition for different pose variations and eye comparisons. Based on the transfer learning approach, it was implemented on seven distinct standard deep-learning-based CNNs [20]. In a feature fusion technique, a multiclass SVM classifier was used to evaluate a mix of HOG and non-handcrafted features for three non-ideal conditions, including the impact of spectacles, the effect of eye occlusion, and posture variations [21].