Publication

Abstract :

The effectiveness, speed, wealth of documentation, community support, integration possibilities, platform freedom, integrated face detection models, and ongoing development make OpenCV the tool of choice for face detection and identification. It includes multiple face recognition methods and is compatible with real-time applications thanks to its streamlined Python code. Although there are competing frameworks, OpenCV is a well-liked option for computer vision applications due to its efficiency and versatility. An 87% accuracy rate is achieved when LSTM networks are integrated for text processing, demonstrating its potential for sequential input analysis. In comparison with previous methods such as LDA, PCA, EBGM, and AAM, the suggested method shows significant improvements. Empirical testing under ideal and real-world conditions confirms the effectiveness of joint identification of numerous faces by associating the corresponding text, even though it increases complexity. This method produces considerable benefits in retrieving information from news articles and resolves accuracy difficulties.