Face Recognition and Identification System for Crime Detection Using Python Programming Language

Abstract

Abstract: Throughout the years, finding criminals has posed a challenging endeavor. In the past, the method relied on leads derived from evidence discovered at the crime scene. Genetic evidence is comparatively straightforward to trace, yet wrongdoers have grown increasingly proficient at obscuring their trail and leaving behind no discernible mark. To address this issue, a face recognition system for crime detection was developed. In this study, a qualitative method was used to collect the face images of criminals in collaboration with law enforcement agencies. An API was built using the criminals National Identification Numbers. Then, a face recognition view that returns the object of the user gotten from the National Identification Number was built. The returned user objects (including face images of the user) underwent preprocessing steps, including normalization, resizing, and noise removal, to enhance the quality and standardize the facial features. Local Binary Patterns algorithm in Python OpenCV library was applied to the preprocessed face images to extract discriminative features from the preprocessed face images. These features aimed to capture the unique characteristics of each face for subsequent recognition. The algorithm was trained on the extracted features and tested given a new input image. The interface component specification of the system was designed using HTML, CSS, and JavaScript client-side technologies.