# Person Identification and Gender Classification Based on Vision Transformers for Periocular Images
###### Abstract  
Many biometrics advancements have been widely used for security applications. This field's evolution began with fingerprints and continued with periocular imaging, which has gained popularity due to the pandemic scenario. CNN (convolutional neural networks) has revolutionized the computer vision domain by demonstrating various state-of-the-art results (performance metrics) with the help of deep-learning-based architectures. The latest transformation has happened with the invention of transformers, which are used in NLP (natural language processing) and are presently being adapted for computer vision. In this work, we have implemented five different ViT- (vision transformer) based architectures for person identification and gender classification. The experiment was performed on the ViT architectures and their modified counterparts. In general, the samples selected for train:val:test splits are random, and the trained model may get affected by overfitting. To overcome this, we have performed 5-fold cross-validation-based analysis. The experiment's performance matrix indicates that the proposed method achieved better results for gender classification as well as person identification. We also experimented with train-val-test partitions for benchmarking with existing architectures and observed significant improvements. We utilized the publicly available UBIPr dataset for performing this experimentation. 202312 December 20221212 December 20221212 December 20221212 December 20221212 January 202212 February 202312 February 202312 February 20231  
performance, circumvention, universality, acceptability, uniqueness, and measurability [1]. The biometric modalities are fingerprint, face, iris, hand geometry, signature, gait, ear, voice, palmprint, hand vein, and teeth [1]. Digital image processing techniques, along with computer vision, pave the way to develop robust systems to integrate biometrics in day-to-day life. Biometric technology is rapidly growing, and the applications are widespread from security at the airport to mobile banking and law enforcement, etc. One of the challenges we have been facing for a few years is the COVID-19 pandemic, which is compelling humans to wear a mask. The features available during face recognition earlier [2] are not available for classification. With the way the new variants and mutations of existing ones are on the rise, alternate methods are necessary to identify subjects at the workplace. It can be either for attendance purposes or to identify persons on the go at any place based on the need. The physical contact with the hand needed for biometrics may cause infectious diseases [3], so we need to explore the possibilities of contactless biometrics. In the scenario of wearing a mask, the challenges faced are plenty since the region of interest is limited, and the central part of the face is covered. When we have signboards saying \"No entry without a mask\", we should have mechanisms to ascertain the person's identity. It is a cause of concern in scenarios where the movement of the public is restricted due to security issues. Figure 1 depicts the sample portion which is not available for identification and highlights indirectly the challenges faced. It is well known that identifying a person based on iris recognition needs good infrastructure, and it is costly and requires cooperation from the user [4]. At the same time, the subject cannot be brought too close to the measuring device because proximity can cause infections. In such a scenario, periocular biometrics is useful for person identification as well as related tasks. A periocular image is defined as the peripheral area of the human eye, which comprises the eyebrows, eyes, and pre-eye orbital portion [5]. To address the issue of person identification using periocular images, several algorithms have been proposed by the researchers. Convolutional neural networks are popular algorithms that helped to achieve competitive performance metrics for visual classification.