Touchless Voting System with RFID Authentication: An Experimental Study

Ansha Antony , Surya Jackson, Muhammed Rasi C. H., Sudharsana Vijayan and Nived Bhaskaran
Department of Electronics and Communication Engineering, Vimal Jyothi Engineering College, Chemperi, India
Correspondence to: Ansha Antony, anshaantony995@gmail.com

DOI: https://doi.org/10.70389/PJS.100222

Additional information

• Ethical approval: N/a
• Consent: N/a
• Funding: No industry funding
• Conflicts of interest: N/a
• Author contribution: Ansha Antony, Surya Jackson, Muhammed Rasi C.H., Sudharsana Vijayan and Nived Bhaskaran – Conceptualization, Writing – original draft, review and editing
• Guarantor: Ansha Antony
• Provenance and peer-review: Unsolicited and externally peer-reviewed
• Data availability statement: N/a

Keywords: Touchless voting, RFID authentication, Ultrasonic gesture control, ESP32 microcontroller, Cloud-based voter database.

Peer Review
Received: 14 August 2025
Last revised: 14 October 2025
Accepted: 17 December 2025
Version accepted: 3
Published: 31 January 2026

Plain Language Summary Infographic

Introduction

Voting is a basic mechanism in democratic cultures, allowing citizens to choose their representatives and influence governance through their participation. A voting system has to be secure, accessible, hygienic, and accurate to be useful and reliable. Yet, the conventional voting practices—like paper ballots and touch-screen electronic voting machines—are beset with numerous drawbacks. These systems tend to use manual verification of voters, physical contact with buttons or screens, and are subject to error, voter impersonation, and slowness in result calculation. Moreover, physical contact is a hygiene issue, particularly during times of health crises such as the COVID-19 pandemic when shared surfaces and busy polling locations pose serious risks.

In order to address these challenges, this project presents a touchless voting system that integrates Radio Frequency Identification (RFID)-based user authentication with gesture-based voting, thereby doing away with physical contact across the voting process. The system is driven by an ESP32 microcontroller and utilizes an RC522 RFID reader module to read RFID cards allocated to every voter. Every RFID card has a unique identifier (UID) associated with a secure cloud-based repository, which holds extensive voter information, such as name, address, and photograph. When scanned, the system cross-checks the UID in real time, shows the associated voter information for verification, and also checks if the voter has already voted. If verification is effective and the voter is eligible, access is given to the voting interface.

The voters then vote by pointing their hands near an ultrasonic sensor that has been assigned to a particular candidate. The voter makes their choice simply by keeping their hand near the sensor, without having to touch any surface. After a vote is cast, the system gives instant feedback in the form of LED indicators, a buzzer, and display. The screen validates the vote by displaying a “Successfully Voted” message with the name of the chosen candidate, enhancing transparency and trust in the system. The status of the voter is then updated in the database to avoid repeated voting attempts. This system enhances security by using automatic identity verification, increases hygiene through the elimination of all physical touch, and is capable of logging data in real-time for exact and transparent handling of results. It is also accessible to disabled people and adjustable for different forms of elections. Through the implementation of contactless RFID authentication as well as sensor-based interaction, the suggested system overcomes one of the principal shortcomings of traditional methods and presents a realistic, effective, and secure solution to contemporary democratic voting.

Literature Review

A Electronic voting systems have been increasingly developing to provide greater security, reliability, and user-friendliness. Various research studies have sought to counter issues like voter impersonation, system complexity, and unhygienic handling. Still, most of these solutions continue to suffer from physical contact, hardware reliance, or non-scalability constraints. Vanitha et al.¹ suggested an RFID-based voting system with biometric fingerprint scanning and GSM notification. Their system employs a two-person control mechanism to trigger and authenticate the voting process. Although this enhances multi-factor security, it adds system complexity and relies on physical touch, which is less hygienic. Our system is based on their RFID-based identity verification but without biometric and GSM components for a completely touchless and simpler interaction.

Mansingh et al.² used RFID and biometric fingerprint matching with Aadhaar database mapping for very high-security voter authentication. While it provides a high degree of accuracy, the process involves the access to the national ID database and physical touch for fingerprint identification. Contrarily, our solution eliminates the requirement of Aadhaar integration and biometric feeding and utilizes a real-time cloud database storing voter name, address, and photograph linked to RFID UID. Therese et al.³ proposed a green cloud-based voting system to enhance data security and minimize energy usage. Their system employs fingerprint sensors and GSM modules for secure communication. Nevertheless, it still relies on touch-based authentication and complicated components. Our project takes their cloud-based idea but substitutes physical biometrics with gesture-based ultrasonic voting, ensuring both hygiene and usability.

Deepa et al.⁴ built a biometric EVM attached to Aadhaar that fetches voter information by using fingerprints. Though it reduces impersonation, it involves physical registration and is not capable of real-time invalidation of votes. Our model overcomes these by employing RFID for verification and invalidating repeat votes in real time in the cloud database. Gokul Krishnan et al.⁵ demonstrated a contactless doorbell system to minimize virus spread via surfaces. Although not related to voting, they used contactless technology, which influenced the hygienic design in our system. Similar to their photodiode and comparator-based design, our ultrasonic sensors recognize gestures by not coming into contact with the user, thus providing a secure user interface.

Zulfiqar⁶ gave hands-on advice on implementing ESP32 with Arduino IDE for Internet of Things (IoT)-based applications. His work justifies ESP32’s applicability in supporting multiple sensors, displays, and cloud services, confirming our utilization of ESP32 as the main microcontroller for real-time processing and database integration. Chavan et al.⁷ suggested a two-stage RFID and biometric system with facial recognition for safe voting. Their focus on preventing double voting and enhancing fairness is commendable. The use of facial recognition along with biometrics, however, adds to system expense and creates privacy issues. Our project streamlines this by eliminating biometric/facial aspects and instead using RFID UID matching with cloud verification and gesture-based voting, providing a hygienic, secure, and more accessible option.

Khokher et al.⁸ have designed an electronic voting system with an ATMega microcontroller for use in college elections. Their design focused on simplicity and on-the-fly computation of results. Yet, it used simple input methods like push buttons and did not employ advanced voter authentication mechanisms. Our project builds upon that by incorporating RFID-based authentication and gesture input, providing a more secure level and contactless experience for voting. Emmanuel et al.⁹ investigated touchless user interfaces for public use to solve hygiene issues during the pandemic. Their solution substituted buttons with infrared sensing and holographic projections. Although not voting-specific, the concept of touch-free human-machine interaction guided the design of our ultrasonic sensor interface, which provides a low-cost and hygienic solution for gesture-based voting.

Tong et al.¹⁰ presented “Scenario,” an RFID signal features-based user-device authentication system in smart IoT scenarios. Their work showed the application of RFID signals for both device and user identification in diverse configurations. This supports the role of RFID sensing in safe, contactless authentication, utilized in our project, albeit with our emphasis on simple UID matching instead of signal features. Kala et al.¹¹ implemented a secure voting system employing both facial recognition and fingerprint biometrics. While this multi-layered authentication considerably enhances identification verification, it poses privacy issues and needs costly hardware. Our system maintains a balance between security and simplicity with the use of only RFID and ultrasonic gesture input to facilitate accessibility, hygiene, and reduced cost. Khan et al.¹² examined the IoT security challenges and suggested solutions such as machine learning (ML) and blockchain to enhance data protection. Although their paper is theoretical, it emphasizes the need for secure communication in cloud-integrated systems. Our project caters to these concerns by employing Firebase Firestore for safe real-time data storage and UID-based verification to ensure integrity and avoid duplicate voting.

Kapur et al.¹³ proposed a contactless EVM with RFID and IR sensors, focusing on hygiene and basic security. Yet, their system still involves keypads and candidate and official passwords for accessing results. Our system takes their contactless idea further by removing all physical inputs and incorporating ultrasonic sensors for voting and automated real-time database updates for result verification. Agarwal et al.¹⁴ suggested a remote biometric voting system based on Aadhaar-linked fingerprints and local voter classification. Although appropriate for national rollout, it relies significantly on Aadhaar infrastructure and physical scanning. Our system provides a more modular and flexible approach by authenticating voters using locally registered RFID cards and double voting prevention using cloud flags. Prasad et al.¹⁵ had already developed a hover-based and face recognition-based touchless voting system. While groundbreaking, facial recognition introduces computational and ethical overhead. Our design borrows from their hover-based design but employs ultrasonic sensors to detect instead of cameras or image processing, hence more viable for large-scale low-cost deployment.

In spite of these mechanical headways, challenges such as information protection, cyber security dangers, and large-scale usage costs stay key concerns. Studies suggested that blockchain, ML, and edge computing can advance upgrade security, guaranteeing the judgment and privacy of votes. Also, the selection of touchless interfacing and contactless verification strategies can move forward voter openness and cleanliness, making EVMs more versatile to cutting edge societal needs (Table 1). To attain a secure and effective electronic voting framework, future investigate ought to center on coordination developing innovations whereas tending to the lawful, moral, and infrastructural challenges related with their sending. Governments and policymakers must collaborate with researchers and innovation designers to set up standardized systems that guarantee security, straightforwardness, and inclusivity in electronic voting. By leveraging progressions in cyber security, manufactured insights, and decentralized innovations, electronic voting can ended up a dependable and reliable arrangement for equitable races around the world.

Table 1: Comparison between touchless voting machine and traditional voting machine based on key operational features.
Sl. no	Comparison between Touchless Voting Machine and Traditional Voting Machine
	Feature	Touchless Voting Machine	Traditional Voting Machine
1.	Vote casting method	Use sensors to detect hand gestures or proximity for selection.	Use physical button or a touchscreen for selection.
2.	Voter authentication	RFID-based automatic authentication with real-time database verification.	Manual verification using ID cards and voters list.
3.	Hygiene and safety	Minimizes risk of contamination.	High risk of contamination due to shared surfaces.
4.	Feedback system	Limited mostly through a display.	Buzzer, LED indicators, and LCD provide real-time feedback.
5.	User Accessibility	More accessible for people with disabilities, as it doesn’t require fine motor control.	Require physical interaction, which may be difficult for some disabled users.
6.	Speed of voting	Faster with automatic RFID authentication and gesture-based selection.	Slower. Due to manual verification and button pressing.
7.	Data storage and logging	Secure cloud or local database storage with real-time updates.	Paper-based or local storage, prone to tampering.
8.	Security	Secure database prevents duplicate and unauthorized voting.	Risk of unauthorized voting, duplicate voting.
9.	Error chances	Automated system minimizes human errors.	Human errors in voter verification and vote counting.
10.	Cost and maintenance	Initial setup cost higher, but minimal maintenance required.	Moderate cost, requires frequent maintenance.

Methodology

The methodology for an RFID-based user authentication system with a touchless voting machine involves several key steps. First, voters are registered, and each is issued an RFID card linked to their details in a secure database. At the polling station, the voter scans their RFID card, and the system verifies their identity and checks if they have already voted. If authenticated, the voter accesses the touchless voting interface, where they select their candidate using proximity sensors instead of physical contact. The system confirms the selection and securely records the vote in an encrypted database while marking the voter as “Voted” to prevent multiple votes. Audit logs are maintained for transparency and security. Finally, thorough testing ensures accurate authentication, secure vote storage, and a smooth voting process before deployment, ensuring a reliable, fraud- resistant, and efficient election system.

RFID Authentication

Figure 1 shows the block diagram of RFID authentication process. The process starts with the RFID card, a digital identity card that is assigned to every registered voter. It is a passive card with a UID that differentiates one voter from another. Being passive, it does not carry its own battery. It derives its power from the electromagnetic field produced by the RFID reader when placed near to it. This UID is not stored encrypted on the card itself but serves as the key to check the eligibility of the voter in the system. Then, the RFID reader module (RC522) reads and identifies the UID from the RFID card. Working at a frequency of 13.56 MHz, the RC522 module generates an electromagnetic field that energizes the card for a short duration and enables data transfer. After the UID is read, the reader transfers it to the ESP32 microcontroller through the Serial Peripheral Interface (SPI). To prevent repeated scanning of the UID because of a card still being in close proximity to the reader, there is a small delay placed immediately after the initial scan so that the user has time to withdraw the card and multiple triggers from the same card are avoided.

The ESP32 acts as the central control unit of the system. After receiving the UID from the RFID reader, it establishes a secure internet connection and sends a request to the cloud-connected database. This database stores registered voter information including the UID, name, address, photo, and voting status. The ESP32 queries the database to check if the UID exists and whether the associated voter has already cast a vote. If the UID is valid and the voter has not voted yet, the ESP32 allows access to the voting interface. If the UID is either invalid or is already tagged as “voted,” the ESP32 stops any further activity and shows a proper error message. The database is a key element in keeping the voting process intact. It serves as the master record holder, storing voter credentials securely and monitoring voting activity. Once a vote is successfully cast, the ESP32 updates the database by marking a flag (e.g., “voted: true”) for that UID, thus disallowing any subsequent voting attempts with the same card. All interactions between the ESP32 and the database are encrypted with HTTPS with TLS to maintain data confidentiality and prevent tampering or interception.

The output devices of the system—a display, LEDs, and a buzzer—give instant feedback at every step. On scanning of the RFID card, the display indicates the name, address, and photo of the voter (if valid) along with a status message: “Valid Voter” or “Invalid Voter”. If valid, the user is then instructed to vote via ultrasonic gesture input. After a vote is cast, the display indicates confirmation of the action with a message like “Successfully Voted for [Candidate Name],” as the buzzer beeps and LED lights flash to offer both visual and audio confirmation. When an invalid or duplicate UID is presented, the display displays an “Invalid Card” or “Vote Already Cast” message to alert the voter and the system to reject the action.

Voting Machine

When successful RFID authentication occurs, the voter is granted access to the voting interface in the secure environment. Here, the system initiates the real voting process, which has been designed to be totally contactless so that hygiene, accessibility, and security against fake activity are all ensured. Such a design is especially crucial for public voting processes where hundreds or thousands of individuals touch the same equipment. The given Figure 2 shows the block diagram of voting process. The procedure starts with the listing of candidate names on a display screen, which is explicitly mapped to their respective ultrasonic sensors. The mapping serves as a guide to indicate the easy recognition of which sensor is associated with which candidate. The visual interface is intuitive and eliminates the necessity for extra physical instructions or signage.

The ultrasonic sensors are the backbone of touchless input. They are the substitutes for conventional physical interfaces such as touchscreens or buttons that are usually hygiene issues. The ultrasonic sensors work by sending high-frequency sound waves and calculating the time it takes for the echo signal to come back after being reflected from the hand of the voter. The microcontroller of ESP32 continuously measures these echo times to determine the distance between the hand and each sensor. In order to guarantee the precision of vote casting, every sensor is implemented with two thresholds, namely distance and time. The distance limit, normally 5–15 cm, guarantees that only intentional hand placements in proximity to the sensor are accounted for, discounting background movement or individuals merely walking by. The time threshold—holding a hand over the space for approximately 2–3 seconds—once again confirms intentionality, discounting unintentional rapid movements or inadvertent activation. This two-factor sensing guarantees that every vote is precise and not taken lightly.

Once the system detects a valid gesture that meets both criteria, the ESP32 logs the vote, associating it with the correct candidate. The system immediately proceeds to provide confirmation feedback, which is crucial for transparency. The screen displays a confirmation message such as “Successfully Voted for [Candidate Name],” and the system simultaneously activates an LED and sounds a buzzer. This combination of visual and auditory feedback reassures the voter that their choice was registered properly. The last step is storing votes. The system will increment the votes for the candidate that has been chosen and store the outcome. Based on design, this data could be stored locally with the use of non-volatile storage like EEPROM or an SD card, or even pushed to a cloud database so it can be aggregated in real-time and accessed remotely. Moreover, the UID of the voter is indicated as “voted” in the database, and hence any subsequent attempts using the same card are invalid and blocked by the system. This provides vote integrity and guards against double voting.

The complete system provides an extremely secure, hygienic, and intuitive voting experience. It is most appropriate for spaces where contactless interaction is unavoidable—during pandemics or within public institutions—and can be conveniently scaled up to larger elections. Through the amalgamation of gesture-based input, real-time cloud connectivity, and visual/auditory feedback, this touchless voting solution stands as a monumental leap over traditional electronic voting machines. The Figure 3 below illustrates the flowchart of the system. Although RFID authentication offers a secure way to verify if live voters are authorized, the system must also prevent deception through fake image sources like photos, videos, or masking images. To address this issue, the proposed system combines liveness detection techniques with RFID authentication. In future updates, artificial intelligence (AI)-based fraud detection can be added to spot unusual voting behaviors. This will help the system resist image spoofing or manipulation.

The system’s vulnerability to hacking, spoofing, or database breaches is a significant concern. To address this, the model includes end-to-end encryption, unique RFID card identifiers, and tamper-proof databases. Secure hashing algorithms protect voter identities and voting records from unauthorized changes. Access control measures and regular audits help reduce the risks of spoofing or data manipulation. With blockchain technology, any attempt to alter stored votes can be detected immediately. Additionally, AI-driven anomaly detection can identify unusual voting patterns in real time, adding extra security against cyber threats. For security features, the model consists of RFID-based voter authentication, touchless sensor-based vote casting (which prevents mechanical tampering), and encrypted storage and transmission of results. These measures protect voter privacy and help stop duplicate or fraudulent voting. By combining physical (RFID, liveness detection) and digital (encryption, blockchain, AI-based fraud detection) security layers, the proposed machine is more robust than traditional electronic voting systems.

Future Scope

The potential of the RFID authentication-based non-contact voting system for the future is immense, with huge possibilities of increasing the security, scalability, and efficiency of the voting process. With advancing technology, the combination of blockchain technology with RFID authentication can highly increase the transparency, integrity, and auditability of votes. The combination will guarantee tamper-proof data storage, auditable transactions, and full protection against vote duplication or forgery. Additionally, using biometric authentication means like fingerprint or facial recognition in combination with RFID cards will enhance voter identity confirmation and root out the vulnerabilities of card sharing or illegal access. Having a cloud-based platform will allow real-time tracking, safe data storage, and remote access to election authorities, which will make it possible to administer large-scale elections with fewer logistical hurdles.

In the second stage, large-scale experimental tests will be run with a larger number of voters (>500 participants) in order to evaluate system correctness, false acceptance/rejection rates, and voter latency under different operating conditions, such as noisy and heavy traffic conditions. These experiments will yield quantitative results to back statistical reporting and system verification. For assuring strong protection, a formal security analysis will be created, including possible cloning, relay, side-channel, and denial-of-service attacks, as well as ballot secrecy, voter anonymity, and end-to-end verifiability considerations. Comparative benchmarking with current secure voting systems like Helios and STAR-Vote will also be investigated to assess comparative performance and resistance.

The incorporation of AI and ML can also increase the system’s capacity to identify anomalies, forecast voter turnout tendencies, and enhance security by recognizing potential attacks in real time. The implementation of IoT-empowered intelligent voting booths can also accommodate automatic support, real-time reporting, and consistent integration between voting units for increased reliability. Future studies will also involve thorough cost and scalability assessment, including bill of materials, per-station cost calculations, and throughput determinations to determine system viability for massive precincts. Advances in RFID technology, including the application of sophisticated passive tags and secure encryption schemes, will further advance system reliability and authentication security.

In addition, the future shall be dedicated to enhancing accessibility for the differently-abled persons, adhering to election laws, data protection acts, and moral voting norms. By considering these factors, the RFID-based non-contact voting system can become a universally implementable, safe, and transparent model applicable not just for political elections but also for organizational voting, university voting, and community-based voting systems. With these developments, the system can potentially serve as a digital voting standard of the next generation, ensuring a more inclusive, open, and technologically enhanced democratic process.

Result and Discussion

Statistical Analysis of Proposed System

The results presented in the Table 2 are obtained from five repeated test runs under uniform environmental conditions. The voting rate was uniform with a low standard deviation of ±0.14 seconds, which means that the system is capable of taking inputs reliably from different users. Hygiene scores exhibited no fluctuation, as system design totally disallows physical contact. Error rates continued to be low and consistent with some variation in early hand placement inconsistencies. Security was highly rated by all users with only minor variation in perceived robustness. The low standard deviations across all parameters indicate the system is reliable and consistent in its operation and supports its effectiveness for real-world deployment.

Table 2: Statistical performance analysis of the proposed touchless voting system (mean ± standard deviation).
Parameter	Proposed System (Mean ± SD)
Speed (seconds)	5.0 ± 0.14
Security (rating)	9.0 ± 0.32
Hygiene (rating)	9.0 ± 0.00
Error Rate (%)	2.2 ± 0.75

The voting system can operate in two modes: standalone (offline) and network-connected (online). In offline mode, votes are stored securely in local memory until they are transferred to the central database later. In online mode, votes are sent to a central server in real time. However, depending on a network can introduce delays, which may slow down large-scale polling. Network latency could temporarily disrupt data synchronization. Still, it would not impact the actual voting process because local storage ensures every vote is kept safe, even during connectivity issues. To improve reliability, the system can use blockchain to record votes, ensuring that storage is tamper-proof and transparent. Each transaction is verified across distributed nodes, making the system resistant to unauthorized changes.

RFID Authentication Accuracy Evaluation

In order to further evaluate the performance of the RFID authentication module, a confusion matrix was created using 100 trials (both valid and invalid inputs). The system proved highly accurate, as indicated below in Table 3:

Table 3: Confusion matrix for RFID authentication accuracy evaluation of the proposed system.
	Predicted Valid	Predicted Invalid
Actual Valid	50	0
Actual Invalid/Used UID	0	50

The calculated metrics are:

• Accuracy: 100%
• Precision: 1.0
• Recall: 1.0
• F1 Score: 1.0

This high accuracy confirms the system’s reliability in authenticating users and rejecting reused or unauthorized cards. The results clearly show that the proposed RFID authentication module performs excellently, with no false positives and no false negatives in the tested trials. These outcomes emphasize the strength of the system in real-world situations where unauthorized access attempts or the reuse of RFID cards could threaten the integrity of the voting process. Achieving 100% accuracy, precision, recall, and F1 score indicates that the system is just as effective at allowing legitimate voters as it is at rejecting fraudulent or duplicate attempts. The curve (Figure 4) shows the classification performance of the RFID authentication system. The system had a True Positive Rate of 1 and a False Positive Rate of 0 when there were no false positives or false negatives in testing, yielding an area under the curve (AUC) of 1.0. This indicates optimal accuracy and perfect capability to distinguish between valid and invalid RFID cards.

The comparison graph (Figure 5) visually represents the performance differences between the Traditional Voting Machine and the Touchless Voting Machine across five key parameters: Voting Speed, Security, Hygiene & Safety, Error Chances, and Accessibility. The green bars indicate the performance of the Touchless Voting Machine, while the red bars represent the Traditional Voting Machine. Higher values (except for Error Chances) indicate better performance.

The Voting Speed of the Touchless Voting Machine is significantly higher than that of the traditional system. This is because the RFID-based voter authentication and ultrasonic sensor-based vote selection eliminate manual processes, making the system faster and more efficient. In terms of Security, the Touchless Voting Machine outperforms the traditional system by preventing unauthorized access and duplicate voting through real-time database verification. In contrast, traditional systems are more vulnerable to human errors and fraudulent voting practices. The Hygiene & Safety factor highlights the biggest advantage of the Touchless Voting Machine. Unlike traditional systems that require physical interaction with buttons or screens, the gesture-based voting mechanism ensures a completely contactless process, making it ideal for pandemic situations and large-scale elections where hygiene is a concern.

The Error Chances parameter (where a lower value is better) shows that traditional systems have higher chances of human errors, such as miscounting votes or verifying the wrong voter. The Touchless Voting Machine significantly reduces errors by automating authentication and vote selection, ensuring accuracy in vote recording. Finally, in Accessibility, the Touchless Voting Machine provides a more inclusive experience. Since no physical buttons or paper ballots are required, people with disabilities can easily cast their votes using hand gestures. Traditional voting machines often lack accessibility features, making it difficult for some voters to participate in the election process. Overall, the graph clearly demonstrates that the Touchless Voting Machine offers significant improvements in speed, security, hygiene, accuracy, and accessibility, making it a more efficient and reliable alternative to traditional voting methods.

Conclusion

The suggested touchless voting system, integrating RFID-based voter identification and ultrasonic sensor-initiated gesture input, provides a new, hygienic, and secure voting alternative to conventional voting schemes. Under laboratory testing, the system registered 100% accuracy in RFID identification and successful, intentional vote registration through ultrasonic gesture detection. With a flawless ROC AUC of 1.0, the system showed outstanding reliability in detecting valid voters and securely recording their choices with no physical contact. The novelty of the system is that it is fully contactless with the use of low-cost, non-biometric technologies—namely RFID and ultrasonic sensing. In contrast to earlier systems based on fingerprints, buttons, or touchscreen interfaces, this solution offers a cleaner, low-maintenance alternative that improves accessibility, security, and public trust—particularly in a post-pandemic era.

Yet, although the outcomes are encouraging, there are some limitations and possible biases in the dataset employed during testing. The testing represented a relatively small and homogeneous population of users in controlled conditions, where lighting was stable and noise was minimal. Actual voters can differ in age, precision of hand movements, and experience with the system. Further, some of the test subjects might have had some knowledge of system behavior, which could make errors less likely. Subjective measurements, such as ratings on hygiene or ease-of-use, can also differ per person. These indicate that greater-scale and heterogeneous testing is required to guarantee performance generalization and system robustness.

In scalability, the system has proved to have the capability to handle high-traffic election scenarios. Both ultrasonic sensors and the RFID module are insensitive to external factors like changes in lighting or overhead lighting, which do not influence their detection capability. The rapid UID read time and immediate sensor input are capable of handling fast voter interactions, an indication that the system would fare well when implemented at busy polling stations with efficient queuing. However, widespread rollout would be subject to some outstanding challenges.

These are the cost of installing RFID readers, ultrasonic modules, and display units in numerous locations, and the requirement for secure, scalable cloud infrastructure to process voter data and votes in real-time. Another important challenge is interoperability with legacy systems in place, which will depend on regional voting technology and standards as requiring software or hardware integration. In summary, the system presents a new and pragmatic method for updating voting technology with a robust focus on hygiene, security, and ease of use. Although preliminary results are outstanding, larger-scale testing, cost analysis, and integration into systems are needed as next steps to ready the system for real-world, high-scale use.

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Cite this article as:
Antony A, Jackson S, Muhammed Rasi CH, Vijayan S and Bhaskaran N. Touchless Voting System with RFID Authentication: An Experimental Study. Premier Journal of Science 2025;15:100222

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