# Voter Authentication Using Enhanced ResNet50 for Facial Recognition
## 1 Introduction
### Paper Structure  
This paper is meticulously structured into five sections, each contributing to a coherent narrative of our research on voter authentication through enhanced facial recognition. As an introduction, this initial section articulates the critical problem of electoral fraud, elucidating our study's motivation and specific objectives. We emphasize the potential of biometric technologies, particularly facial recognition, to mitigate multiple voting and reinforce electoral integrity. A concise overview of our proposed system is provided, highlighting components and their synergistic integration for high accuracy and robustness. This introduction lays the groundwork for a detailed exploration of the existing literature, our innovative methodology, comprehensive experimental results, and insightful concluding remarks. Section 2 presents a rigorous review of the existing literature on facial recognition and detection methods. Going beyond a mere list of previous work, this section critically analyzes the strengths and limitations inherent in various approaches, from traditional feature-based techniques to State-of-the-Art deep learning architectures. We examine the nuances of algorithms such as Haar cascades, HOG, and LBP, alongside contemporary convolutional neural networks (CNNs) like VGGNet, Inception, and MobileNet. Furthermore, we evaluated the characteristics and suitability of commonly used facial recognition datasets, including LFW, PubFig, and MegaFace. A key focus is placed on the performance metrics used to assess the accuracy and efficiency of facial recognition systems, such as precision, recall, F1 score, and ROC curves. This comprehensive review establishes a solid foundation for understanding the novelty and significance of our proposed approach. Section 3 meticulously details our proposed methodology, providing a step-by-step explanation of the development and implementation of our ResNet50-based facial recog \begin{table}
\begin{tabular}{l l l} \hline
**Approaches** & **Techniques** & **Accuracy** \\ \hline [PERSON] et al. [15] & AlexNet & 91.8\% \\ \hline [PERSON] et al. [16] & FaceNet & 98.87\% \\ \hline [PERSON] et al. [17] & ConvNet & 98.95\% \\ \hline [PERSON] et al. [18] & GoogleNet & 99.1\% \\ \hline [PERSON] et al. [19] & Transfer learning with ResNet50 & 99.33\% \\ \hline
**Proposed** & **MTCNN + ResNet50** & **99.56\%** \\ \hline \end{tabular}
\end{table} 
Table 1: Face detection result on LFW dataset. nition system. We begin by describing the facial image collection process and specifying the hardware and software utilized to capture voter images at polling stations. We then elaborate on the data pre-processing and augmentation techniques employed to enhance the robustness of our model, addressing challenges posed by variations in lighting, pose, and expression. A critical aspect of this section is a detailed explanation of the feature extraction process, highlighting the integration of the MTCNN algorithm for accurate face detection and the ResNet50 architecture for robust feature embedding. We thoroughly discuss the ArcFace loss function, elucidating its role in optimizing the angular margin between different classes and improving the discriminative power of the model. Finally, we describe the creation of the secure facial image database for voter registration and the feature comparison method using Mahalanobis distance for reliable identity verification. Section 4 presents a comprehensive evaluation of the performance of our system, analyzing the results obtained through rigorous experimentation. We present key metrics such as accuracy, precision, recall, F1-score, and area under the ROC curve (AUC), demonstrating the effectiveness of our approach in accurately identifying voters. These metrics are evaluated using a diverse set of facial images, carefully selected to reflect the real-world conditions encountered at polling stations.