Comparative Study of Modern Biometric Authentication Techniques for Secure Access Systems
This Biometric authentication systems verify personal identity by examining unique human traits related to physical structure and behavioral patterns, offering a secure and efficient alternative to traditional knowledge-based access controls. This study presents a systematic comparison of five commonly deployed biometric methods: fingerprint verification, iris scanning, facial analysis, voice pattern recognition, and signature verification. Each technique is examined using seven evaluation dimensions, including identification precision, execution speed, implementation cost, protection strength, system dependability, individual distinctiveness, and public acceptance. The analysis is based on findings reported in recent peer-reviewed research published between 2020 and 2025. The results indicate that fingerprint authentication remains the most feasible option for widespread everyday use due to its affordability, rapid processing, and operational simplicity. Meanwhile, iris-based systems demonstrate exceptional robustness, accuracy, and resistance to manipulation, making them highly suitable for critical security applications. The study further highlights that hybrid biometric frameworks, which combine multiple identification techniques, can significantly improve system effectiveness by enhancing security coverage, adaptability, and long-term scalability.
In today's rapidly evolving digital landscape, verifying an individual's identity plays a fundamental role in ensuring information security. Traditional authentication methods such as passwords and tokens, although once considered dependable, are no longer sufficient to safeguard sensitive data from advanced cyber threats (Sharma & Yadav, 2021). Modern attackers frequently exploit password breaches and social engineering tactics to gain unauthorized entry. As a result, biometric authentication - relying on distinctive physical or behavioral characteristics - has emerged as a more secure and dependable alternative.
Biometric systems utilize unique human identifiers such as fingerprints, iris patterns, facial structures, and vocal characteristics to create a form of identification that cannot be shared or replicated. These biological traits typically remain constant throughout an individual's life, which enhances the accuracy and dependability of biometric verification compared to traditional credentials (Agarwal & Bansal, 2023). The widespread adoption of biometric technology in smartphones, border security, financial services, and healthcare sectors illustrates both its scalability and its increasing public trust (Rahman et al., 2022).
Nevertheless, the performance of biometric systems is influenced by environmental and technical factors. Elements like lighting, background noise, sensor precision, and user participation can significantly affect the accuracy of results. For instance, facial recognition may perform poorly under low-light conditions or partial obstruction, while fingerprint scanners can encounter difficulties when the user's skin is dry or damaged (Kaur & Kumar, 2024). Hence, it is crucial to conduct an impartial comparative analysis of different biometric techniques to determine which approaches best meet the requirements of secure authentication systems. This study comparatively evaluates five commonly used biometric modalities - fingerprint, iris, facial, voice, and signature recognition - based on seven major criteria: accuracy, speed, cost efficiency, reliability, security, uniqueness, and user acceptance. The analysis integrates findings from recent academic and industrial research (2020–2025) to reflect current advancements and practical implementation challenges in biometric authentication. The purpose of this research is to identify efficient and reliable biometric techniques for general and high-security applications. The study highlights strengths and limitations and recommends multi-modal biometric systems for enhanced performance and fraud resistance.
Fig. 1: Advantages and Disadvantages of Biometrics.
Types of Biometric Techniques
Biometric identification systems are generally categorized into two principal types: physiological and behavioral biometrics.
1). Physiological Biometrics
This category relies on physical features that are inherent to the human body and remain mostly unchanged throughout life. Examples include:
2). Behavioral Biometrics
Behavioral systems assess patterns that emerge from a person's habits or movements, which are unique yet may vary slightly over time.
Examples include:
Fig. 2: Types of Biometric Techniques.
The Standard Design of an Automatic Biometric System
Biometric System Overview
A biometric system is a technology-based process designed to identify or verify individuals by analyzing their distinct physical or behavioral traits. As explained by Malik and Devi (2020), such systems function in two primary stages: enrollment and authentication. During enrollment, biometric characteristics are recorded and saved, while in authentication, the system compares new input data against the stored templates to confirm identity. A biometric recognition framework includes several interconnected modules that operate sequentially to achieve accurate identification.
Sensor / Data Acquisition Module
This module collects the user's biometric data - such as a fingerprint, iris scan, facial image, or voice sample - serving as the initial input stage of the system. The accuracy of recognition largely depends on the sensitivity and precision of the sensing device (Zhang et al., 2023).
Feature Extraction Module
After data capture, the feature extraction module processes it to highlight distinctive patterns unique to an individual. These processed data points are converted into a numerical representation, commonly known as a biometric template (Verma & Jain, 2023).
Template Database / Storage
This database securely stores the extracted templates. Each record is encrypted and connected to a user identifier for retrieval. Strong database management is crucial to ensuring user privacy and data security (Kumar & Ahmad, 2025).
Matching Module
During this stage, the system compares a newly captured template to stored templates - either one-to-one for verification or one-to-many for identification. The module calculates a similarity score to indicate the degree of correspondence (Agarwal & Bansal, 2023).
Decision Module
Based on the similarity score, the decision module determines the outcome of the comparison. It verifies the user's claimed identity or identifies them among several registered individuals depending on the application settings. (1:1 comparison) or identify a user from a group (1: N comparison) (Rashid et al., 2021).
Fig. 3: The standard design of an automatic Biometric system.
Kim and Lee, (2020) observed a rising trend toward passwordless authentication due to increased remote work and cloud service dependency. They highlighted that biometric systems outperform traditional credentials in both usability and resistance to phishing attacks. Rashid et al. (2021) compared several biometric modalities and concluded that fingerprint and iris recognition offer high accuracy and scalability. However, both can face limitations from sensor wear and environmental impacts, suggesting the need for periodic calibration.
Iqbal and Rahman, (2021) emphasized the importance of lighting and image quality in facial recognition systems. Despite algorithmic advances, such conditions still affect accuracy by up to double-digit percentages in uncontrolled environments. Hasan and Noor, (2022) studied voice biometrics used in contact centers and smart devices. They found voice recognition effective yet vulnerable to background noise and mimicry, highlighting the importance of robust signal processing. Thapa and Singh, (2022) assessed signature recognition systems and classified them as suitable for low-cost, low-security contexts, while noting higher susceptibility to forgery compared with physiological biometrics.
Zhang et al. (2023) reported that iris recognition provides near-perfect accuracy and permanence with very low false acceptance rates, but requires high-quality infrared sensors, raising costs and limiting mass adoption. Patel et al. (2024) examined hybrid biometric systems combining fingerprint and face data. Their research showed that fusion enhances reliability and reduces spoofing incidents, improving both usability and security. Kumar and Ahmad, (2025) explored ethical and legal dimensions of biometric data handling, arguing for stronger data protection policies and transparent governance frameworks to safeguard users' privacy in digital ecosystems.
This study utilizes a descriptive comparative research design based on secondary data from peer-reviewed journals, institutional reports, and case studies published between 2020 and 2025. Five biometric systems -fingerprint, iris, facial, voice, and signature recognition - were analyzed using seven performance parameters: accuracy, speed, cost efficiency, security, reliability, uniqueness, and user acceptance. The collected data were compiled and represented using a bar chart to visually compare the strengths and weaknesses of each method.
Comparative Analysis
Fig. 1 presents comparative Spider Chart displaying performance ratings (1–5 scale) of five biometric methods across seven parameters. Fingerprint recognition performed highest overall, excelling in accuracy, speed, and affordability. Iris recognition followed closely, with unmatched security and uniqueness but lower user acceptance due to higher cost. Face recognition scored moderately - convenient and contactless but sensitive to lighting variations. Voice recognition exhibited convenience yet noise sensitivity, while signature verification scored lowest overall, performing best only in user familiarity.
Fig. 4: Comparative Spider Chart of Biometric Techniques Across Seven Parameters (1–5 scale).
The results reveal that fingerprint recognition is the most balanced and widely adopted method, offering high accuracy, speed, and affordability. Iris recognition remains the most secure and precise but is less accessible due to higher costs and specialized sensors.
Facial recognition demonstrates strong usability and integration potential but suffers under environmental variability. Voice and signature systems are affordable yet relatively insecure. Overall, combining multiple biometric modalities enhances both security and usability.
This study compared five biometric authentication methods based on seven performance parameters using data from 2020–2025. Results indicate that fingerprint recognition remains the most practical method for general use, while iris recognition is preferable for high-security scenarios. Organizations should consider multi-modal biometric systems - such as fingerprint plus iris - to improve reliability and reduce spoofing risk. Future research should focus on low-cost iris sensors, resilient facial recognition in variable lighting, and privacy-preserving frameworks for biometric data.
The author thanks and appreciate the guidance and constructive suggestions provided by the academic supervisors throughout the design, implementation and writing of this successful study.
Author has no conflicts of interest related to this study.
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Academic Editor
Dr. Phelipe Magalhães Duarte, Professor, Faculty of Biological and Health Sciences, University of Cuiabá, Mato Grosso, Brazil
Department of Computer Science, Bakhtar University, Kabul, Afghanistan
Gharjistani SM. (2026). Comparative study of modern biometric authentication techniques for secure access systems. Am. J. Pure Appl. Sci., 8(1), 503-509. https://doi.org/10.34104/ajpab.026.05030509
