Revolutionizing Forensic Identification: A Dual-Method Facial Recognition Paradigm for Enhanced Criminal Identification

In response to the challenges posed by increasingly sophisticated criminal behaviour that strategically evades conventional identification methods, this research advocates for a paradigm shift in forensic practices. Departing from reliance on traditional biometric techniques such as DNA matching, eyewitness accounts, and fingerprint analysis, the study introduces a pioneering biometric approach centered on facial recognition systems. Addressing the limitations of established methods, the proposed methodology integrates two key components. Firstly, facial features are meticulously extracted using the Histogram of Oriented Gradients (HOG) methodology, providing a robust representation of individualized facial characteristics. Subsequently, a face recognition system is implemented, harnessing the power of the K-Nearest Neighbours machine learning classifier. This innovative dual-method approach aims to significantly enhance the accuracy and reliability of criminal identification, particularly in scenarios where conventional methods prove inadequate. By capitalizing on the inherent uniqueness of facial features, this research strives to introduce a formidable tool for forensic practitioners, offering a more effective means of addressing the evolving landscape of criminal tactics and safeguarding the integrity of justice systems.