## 1 Introduction  
The rapid advancement of artificial intelligence (AI) has achieved significant progress and, thereby, has gained substantial attention worldwide. Since the emergence of deep learning (a core technology of AI), the applications of AI technology have been expanded to various fields for increased convenience in daily human life. In addition, it has improved the quality of life through its applications in the medical, agricultural, financial, and autonomous vehicle fields. Notwithstanding the positive influences of technology advancement and diffusion, certain risks are imposed on humans as intelligent cyber-attacks increase [1]. An array of personal authentication technologies has been studied to defend against similar threats. Personal information has been protected using passwords or OTPs. However, recently, technologies using biometrics for protecting personal information have been employed increasingly to address the risk of loss or theft. These technologies can conveniently and safely manage personal information as well as verify the identity of users. As a consequence, personal authentication technology using biometric information such as voice, gender, face, and behavior is being actively developed and used [2, 3]. Seven characteristics are required for biometric recognition. Fundamental characteristics such as universality (indicates whether every individual has the information), uniqueness (indicates the distinctiveness of each individual's information), permanence (indicates whether the information remains unaltered over time and is unmodifiable), and collectability (indicates whether the biometric can be acquired conveniently using sensors) are required. The following characteristics are required for trusting and using biometrics: accuracy including the speed and precision of receiving and processing information, accessibility that does not induce reluctance to biometric measurement, and safety against inappropriate use to achieve deception security [4]. Biometric recognition involves behavioral characteristics such as voice, gait, electrocardiogram (ECG), and brain waves as well as physical characteristics including the face, fingerprints, and the iris. The advantage of face recognition is that facial features can be identified rapidly and conveniently. However, recognition may be ineffective when facial expressions or lighting vary. Fingerprint recognition is used widely. Here, application sensors are used to capture fingerprint images. Although fingerprint recognition is simple, it is sensitive to skin wounds and impurities and can be forged or tampered with. However, recently, face or fingerprint recognition has been difficult to implement in public places during the pandemic because of the need for everyone to wear facemasks. Although voice recognition is a convenient and safe technology because it uses features extracted from voices, recognition may be difficult because of the use of a recorded file or the presence of noise. Gait recognition is a technology that involves an analysis of a user's gait characteristics, which is considerably influenced by the surrounding environment. The application of biometric information expressed outside the body is encountering severe problems owing to personal damage caused by user recognition error as well as forgery and tampering. Accordingly, research is being actively conducted on personal identification using internal signals of the human body, such as electroencephalogram (EEG), electromyogram (EMG), and ECG. EEG is a test that measures an electrical signal at the scalp. It is utilized in brain-computer interfaces (BCIs) [5] and medical diagnosis. However, humans are reluctant to attach sensors to their head for signal measurement. Furthermore, signals may be distorted as these pass through the cranial bone. EMG records the signals generated by muscles. It is utilized in motion recognition, medical diagnosis, and rehabilitation treatment. However, signals must be set for each motion.