Face Detection-Based E-Voting Machine: A Secure & Intelligent Biometric Voting System

Face Detection-Based E-Voting Machine: A Secure & Intelligent Biometric Voting System

Arpit Kumar Gupta, Akash Pandey, Gaurav Yadav

B. Tech Students,

Department of CS-AI (Computer Science & Artificial Intelligence)

Galgotias College of Engineering and Technology, India

Mr. Krishna Murari

Assistant Professor Department of CS-AI (Computer Science

& Artificial Intelligence)

Galgotias College of Engineering and Technology, India

Abstract – Ensuring the authenticity of every voter is the foundational challenge of any democratic process. Conventional paper-based ballots and rudimentary electronic voting terminals remain vulnerable to identity fraud, booth-capturing, and double-voting. This paper presents the design, implementation, and experimental evaluation of a Face Detection-Based Electronic Voting Machine (FD-EVM) that integrates real-time biometric authentication with a tamper-resistant vote-recording mechanism. The proposed architecture couples a Raspberry Pi 4 microcomputer with a high-definition camera module, deploying the Local Binary Pattern Histogram (LBPH) face recogniser alongside a Haar Cascade detector. A multi-layered security protocol first verifies the voter's Aadhaar-linked face template, then issues a one-time voting token that is invalidated immediately after a ballot is cast. Experimental results on a dataset of 150 registered subjects demonstrate a recognition accuracy of 97.3%, a false acceptance rate (FAR) of 0.9%, and a false rejection rate (FRR) of 1.8%, while end-to-end voting latency remains under 4.2 seconds. The system eliminates proxy voting, maintains voter anonymity, and produces an auditable encrypted log, making it a practical step towards modernising electoral infrastructure in developing nations.

Keywords – e-voting; face recognition; LBPH; Haar Cascade; biometric authentication; Raspberry Pi; electoral security; FAR; FRR.

1. INTRODUCTION

   Democratic governance rests upon the twin pillars of free expression and secure ballot casting. Over the past three decades, nations worldwide have piloted electronic voting machines (EVMs) as a replacement for manual voting to reduce counting errors, lower logistical costs, and accelerate result declaration. Yet widespread deployment has been accompanied by fresh threats: identity impersonation, booth management, and software tampering continue to undermine public confidence in digital electoral processes.

   Biometric authenticationencompassing fingerprint, iris, and face modalitiesoffers a path towards voter verification that is both non-repudiable and convenient. Among these, facial recognition stands out for its contactless nature, affordability of hardware, and growing

   robustness under real-world illumination variance. When embedded directly into the voting terminal, it allows officials to confirm voter identity without physical contact, a factor that gained particular importance during and after the COVID-19 pandemic.

   This paper makes the following principal contributions:

   1. A novel integrated architecture combining face detection, recognition, and one-time token issuance on an embedded platform.

   2. A systematic comparison of Haar Cascade and DNN-based face detection frontends for low-power voting terminals.

   3. An encrypted audit-trail module that preserves vote secrecy while enabling post-election verification.

   4. Rigorous empirical evaluation with 150 subjects across diverse lighting conditions and age groups.

      The remainder of this paper is organised as follows. Section II surveys related work. Section III details the system architecture and subsystem design. Section IV explains the face recognition pipeline. Section V describes the security model. Section VI presents experimental findings. Section VII discusses limitations and future work, followed by conclusions in Section VIII.

2. RELATED WORK

   The intersection of biometrics and electronic voting has attracted sustained research attention. Bhagat and Wadekar (2018) demonstrated a fingerprint-based EVM prototype using an Arduino microcontroller and R307 sensor, achieving satisfactory authentication under controlled laboratory conditions. However, fingerprint scanners are susceptible to spoofing with gelatin replicas and degrade under humid environments common in tropical polling booths.

   Khan et al. (2019) proposed a dual-modality system combining fingerprint and facial verification to reduce the single-factor failure rate. Their system attained an equal error rate (EER) of 2.1%, yet its computational demands exceeded the capabilities of low-cost microcontrollers, necessitating server-side inference with associated latency and network dependency.

   Harakannanavar et al. (2020) applied deep convolutional networks to face-based voter authentication, reporting recognition accuracy exceeding 99% on the LFW benchmark. Nevertheless, the large model footprint (over 250 MB) precluded deployment on embedded devices,

   limiting practical usability in resource-constrained polling environments.

   Subsequent work by Rao and Lakshmi (2021) explored iris recognition for EVMs, noting that while iris patterns yield low FAR, the specialised near-infrared illumination hardware inflates device cost considerably. Sharma et al. (2022) revisited LBPH-based face recognition on Raspberry Pi and showed that, with appropriate preprocessing, accuracy above 95% is achievable under moderate lighting variance. Their work, however, did not integrate an end-to-end voting workflow or security audit trail.

   The present study bridges this gap by co-designing the biometric subsystem and the voting workflow, introducing a token-invalidation mechanism that prevents double voting without requiring network connectivity during ballot casting.

3. SYSTEM ARCHITECTURE

   The FD-EVM is conceived as a self-contained kiosk comprising five principal subsystems: a biometric capture unit, a recognition engine, an authentication and token manager, a ballot casting interface, and an encrypted log generator. Figure 1 presents the high-level block diagram.

   1. Hardware Platform

      The processing core is a Raspberry Pi 4 Model B (4 GB RAM, Cortex-A72 quad-core at 1.8 GHz). A Sony IMX219 8-megapixel camera module is mounted at eye level within the kiosk enclosure. A 7-inch capacitive touchscreen presents the candidate list; tactile backup buttons are provided for accessibility compliance. A dedicated 128 GB microSD card stores the encrypted voter registry and audit log. Power is supplied through an uninterruptible power supply capable of sustaining six hours of operation without mains electricity.

   2. Software Stack

      The system runs Raspbian OS (64-bit, Bookworm). The biometric pipeline is implemented in Python 3.10 using OpenCV 4.8. The graphical interface is built with Tkinter augmented by custom theme overlays. SQLite 3 manages the voter registry with AES-256 field-level encryption provided by the PyCryptodome library. Systemd service units ensure automatic restart and tamper detection on unexpected shutdown.

   3. Operational Flow

   Upon voter arrival at the terminal, the system transitions from a locked standby state to active capture mode when a presiding officer scans a QR session token. The voter faces the camera; within one second the detector localses the face region and the recogniser queries the encrypted registry. On successful match, a single-use voting token is issued and the ballot screen activates. After the voter selects a candidate and confirms, the token is immediately invalidated and the vote is appended to the encrypted audit log. The system then returns to standby, ready for the next voter.

4. FACE RECOGNITION PIPELINE

   1. Pre-Processing

      Each captured frame (1280×720 pixels) undergoes the following pre-processing chain. First, Contrast Limited Adaptive Histogram Equalisation (CLAHE) is applied with a clip limit of 2.0 and tile size of 8×8 to normalise luminance heterogeneity across the face region. The image is then converted to CIE Lab colour space and the L channel

      alone is forwarded to the detector, reducing sensitivity to colour temperature variations in ambient lighting.

   2. Face Detection

      Haar Cascade detection (frontal-face model trained on 10,000 positive and 5,000 negative samples) was selected as the primary detector owing to its sub-50 ms inference time on the Raspberry Pi 4. Detection parameters were empirically optimised: scale factor 1.15, minimum neighbours 5, minimum face size 80×80 pixels. When the cascade fails to return a detection after three consecutive framesa situation observed mainly when the voter approaches from an extreme anglea lightweight DNN-based detector (ResNet-10 SSD, OpenCV DNN module) is invoked as fallback, adding at most 180 ms.

   3. Landmark Alignment

      Dlib's 68-point facial landmark predictor is applied to each detected face bounding box. The eye-centre coordinates are extracted and used to compute an affine transformation that rotates and scales the face chip to a canonical 128×128 pixel representation with a fixed inter-ocular distance of 60 pixels. This alignment step reduces intra-class variation and improves LBPH discriminability markedly.

   4. Feature Extraction and Matching (LBPH)

   Local Binary Pattern Histogram (LBPH) operates by thresholding an 8-neighbour ring of radius 1 around each pixel against the central pixel value and concatenating the resulting binary code into a uniform pattern. The face image is divided into a 4×4 grid of cells; each cell yields a 256-bin histogram, and the 16 histograms are concatenated into a 4096-dimensional feature vector. Recognition is performed via Chi-squared distance comparison against all enrolled templates. A voter is accepted if the minimum distance falls below an empirically set threshold ( = 65), otherwise the attempt is rejected.

   Training phase: For each registered voter, 20 images are captured under five distinct lighting conditions (bright overhead, dim, side-lit, back-lit, outdoor). This diversity ensures that the stored model adequately represents realistic polling-booth variations.

5. SECURITY MODEL

   1. Anti-Spoofing

      A passive liveness detection module analyses facial texture frequency to distinguish live faces from printed photographs or screen replays. It computes the power spectrum ratio (PSR) in the high-frequency band of the CLAHE-processed image. Faces with PSR below a calibrated threshold (0.35) are classified as non-live and the authentication attempt is blocked. This lightweight measure, requiring no additional hardware, rejected 100% of printed-photo attacks and 94% of screen-replay attacks in bench testing.

   2. One-Time Token Protocol

      Upon successful biometric verification, the authentication manager generates a cryptographically random 256-bit token using os.urandom and associates it with the voter's anonymised hash identifier. The token is stored in a volatile in-memory dictionary with a 120-second expiry timer. Vote casting consumes and deletes the token; attempting to vote a second time finds no valid token for that session. This mechanism guarantees that even if an adversary records a successful biometric response, they cannot replay it to cast an additional ballot.

   3. Encrypted Audit Log

      Each vote record consists of: (i) a BLAKE2 hash of the voter's anonymised identifier, (ii) an encrypted candidate field, and (iii) a

      UNIX timestamp. Records are appended to an SQLite database where candidate fields are encrypted with AES-256-GCM under a key held in a TPM-emulated secure element. The hash chainwhere each record's digest incorporates the previous record's digestprovides tamper evidence equivalent to a lightweight blockchain structure without requiring consensus overhead.

   4. Physical Security

   The kiosk enclosure is sealed with tamper-evident screws. An onboard accelerometer detects tilt events exceeding 15 degrees and triggers an immediate session lock. All USB ports are disabled via device tree overlays, permitting only the camera and touchscreen interfaces. The microSD boot partition is protected by dm-verity, invalidating the boot sequence if system files are modified between power cycles.

6. EXPERIMENTAL RESULTS

   1. Dataset and Setup

      Experiments were conducted with 150 volunteer participants (82 male, 68 female) aged 18 to 74 years drawn from a university campus. For each participant, 20 training images and 10 test images were acquired under controlled and uncontrolled illumination conditions, yielding 1500 test samples in total. Evaluation metrics include recognition accuracy (ACC), false acceptance rate (FAR), false rejection rate (FRR), equal error rate (EER), and average voting latency.

   2. Recognition Performance

      Table I summarises performance across three threshold settings. The selected threshold = 65 achieves the best balance between security (low FAR) and usability (low FRR). The system attained an overall recognition accuracy of 97.3% on the full test set. Performance was marginally lower for participants over 65 years due to greater skin-texture variability; future enrolment guidelines will require a broader training image set for elderly voters.

      TABLE I. RECOGNITION PERFORMANCE AT VARYING THRESHOLDS

      Threshold ()	ACC (%)	FAR (%)	FRR (%)	EER (%)
      55	96.1	0.4	3.5	1.95
      65 (Chosen)	97.3	0.9	1.8	1.35
      75	95.8	2.1	2.1	2.10

   3. Latency Analysis

   End-to-end voting latencyfrom face appearing in camera frame to vote confirmation screenwas measured across 300 sessions. The mean latency was 4.18 seconds ( = 0.62 s). The face detection step (Haar Cascade) consumed an average of 42 ms; CLAHE and alignment added 31 ms; LBPH matching across 150 templates required 190 ms; database write and encryption took 80 ms; the remaining 3.8 seconds were attributed to voter interaction (candidate selection and confirmation).

   System	Modality	Platform	ACC (%)	FAR (%)	Online Required
   Bhagat et al. [5]	Fingerprint	Arduino	93.4	2.8	No
   Khan et al. [6]	Face+FP	Serve	97.9	0.8	Yes

   TABLE II. COMPARISON WITH RELATED SYSTEMS

   Sharma et al. [9]	Face (LBPH)	RPi 3B	95.2	1.4	No
   Proposed FD-EVM	Face (LBPH)	RPi 4B	97.3	0.9	No

   Table II demonstrates that the proposed FD-EVM achieves competitive accuracy with the best-reported system while retaining complete offline operabilitya critical requirement for polling booths in remote or low-connectivity regions.

7. DISCUSSION AND FUTURE WORK

   The results affirm that LBPH-based face recognition on an edge device can meet practical accuracy requirements for biometric voting authentication without dependency on cloud infrastructure. The 0.9% FAR is low enough to prevent the vast majority of impersonation attempts; electoral rules typically allow human override by a presiding officer in the rare case of biometric rejection, mitigating the inconvenience of the 1.8% FRR.

   The primary limitation of the current system is its susceptibility to advanced 3D mask spoofing attacks, which the PSR-based liveness detector does not fully counter. Future iterations will incorporate depth estimation via structured light or a stereo camera pair to enable robust liveness detection without prohibitive cost increase. Additionally, the scalability of the flat LBPH template store warrants replacement with an approximate nearest-neighbour index (e.g., FAISS) as constituency sizes grow beyond 10,000 registered voters.

   From a usability standpoint, the interface will be enhanced with multilingual support and audio guidance for visually impaired voters. Integration with a national Aadhaar API (subject to regulatory approval) would automate the voter enrolment process, reducing administrative burden and potential human-error during pre-election setup.

8. CONCLUSION

This paper has presented a comprehensive design and implementation of a face detection-based electronic voting machine. The system tightly integrates real-time biometric verification with a cryptographic voting protocol on an affordable embedded platform, achieving 97.3% recognition accuracy, 0.9% FAR, and sub-5-second end-to-end latency in empirical trials. The one-time token mechanism effectively prevents double voting without requiring network connectivity, and the hash-chained encrypted audit log provides post-election verifiability while preserving ballot secrecy. The proposed FD-EVM outperforms comparable offline systems in accuracy and matches the best server-dependent solutions without their connectivity constraint.

By addressing identity fraud at the point of ballot issuance rather than relying solely on voter roll checks, the FD-EVM offers a technically grounded, cost-effective pathway to more trustworthy elections. With the planned enhancements in liveness detection and multilingual accessibility, this architecture is well-positioned for real-world electoral pilot deployments.

ACKNOWLEDGEMENT

The authors sincerely thank the Department of Computer Science & Engineering, XYZ Institute of Technology, for providing laboratory facilities and the volunteer participants whose cooperation made this study possible. No external funding was received for this research.

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