International Journal of Science, Engineering and TechnologyAuthors: Khatik Huzaifa Mohammad Aarif, Dr. Jasbir Kaur, Professor Sandhya Thakkar
Abstract: Criminal face detection is a critical aspect of law enforcement and public safety. This project explores the application of machine learning and computer vision techniques to identify potential criminal faces from images. The methodology involves preprocessing image data, extracting facial features using deep learning models like Convolutional Neural Networks (CNNs), and implementing facial recognition algorithms .The project utilizes popular Python libraries such as Open CV , Tensor Flow, and Keras to train and deploy the models. Additionally, a dataset comprising diverse facial images is employed for model training and evaluation. The trained model's performance is assessed using metrics such as accuracy, precision, recall, and F1 score. Results demonstrate the feasibility of using machine learning algorithms to detect potential criminal faces with a certain degree of accuracy. Ethical considerations regarding biases in data and implications of using such technology in law enforcement are discussed. Further research directions are suggested to enhance the robustness and fairness of criminal face detection systems. This abstract provides a high-level overview of the project's objectives, methodologies, findings, and potential ethical considerations without getting into specific code implementations or technical details.
