"Dual-Model Machine Learning Framework for Gender Classification: Advancing Cyber & Digital Forensic Applications Through Facial Biometrics"
Sharma Paras · Revista Electronica de Veterinaria · 2024
This research explores an advanced machine learning framework for gender classification using facial measurements, specifically designed for digital & cyber forensic and biometric applications. The study leverages 14 precise facial measurements to investigate and compare ensemble methods and deep neural networks, achieving a notable accuracy of 82.76%. Through rigorous preprocessing, feature selection, and optimization techniques, the findings demonstrate the dual-model system's robustness and practical utility in digital & cyber forensics. The research methodology encompasses a comprehensive approach, integrating state-of-the-art machine learning algorithms with anthropometric data. By employing a diverse set of facial measurements, the study captures intricate gender-specific features, enabling a more nuanced and accurate classification process. The ensemble methods and deep neural networks are meticulously fine-tuned to extract and interpret complex patterns within the facial data, resulting in the high accuracy rate. This work significantly advances the field of automated biometric analysis, offering valuable insights for both theoretical research and practical implementation. The dual-model system's performance underscores its potential for enhancing cyber & digital forensic investigations and biometric identification processes. Moreover, the study's findings contribute to the broader understanding of gender-based facial characteristics and their applications in machine learning. The research's implications extend beyond gender classification, potentially influencing various domains such as security systems, human-computer interaction, and personalized technology. By demonstrating the efficacy of facial measurements in gender determination, this study paves the way for further exploration of biometric-based solutions in diverse fields. In conclusion, this cutting-edge research not only achieves high accuracy in gender classification but also provides a robust framework for future advancements in digital & cyber forensics and biometric applications. The study's comprehensive approach and promising results position it as a significant contribution to the evolving landscape of machine learning and biometric analysis.