EFFICIENT FACE DETECTION USING VIOLA-JONES ANDNEURAL NETWORKS: A COMPARATIVE STUDY

Face detection technology underpins most of today’s face recognition systems and isvaluable in many industries, including security, healthcare, retail, and entertainment. This study aims tofuse classical computer vision methods, like the Viola-Jones algorithm, with contemporary techniques indeep learning, for instance, Feed Forward Neural Networks (FFNN), to improve face detection systems inaccuracy, efficiency, and reliability. The proposed system uses the Viola-Jones algorithm for preliminaryface detection and an FFNN for feature extraction and classification achieving 98.5% accuracy on variousdatasets. The system works well under a variety of conditions such as lighting, angle, and occlusions, andhas real-time performance with frame rates between 15-20 FPS. Results confirm that the system is moreaccurate than applying the Viola-Jones method alone and has the same accuracy as CNN-based modelswhile needing less computational resources. This approach is useful and effective for practical applicationslike security surveillance, biometric identification, or human-computer interaction because they are morerapid and easier to deploy.