Detecting occluded faces in unconstrained crowd digital pictures

Face detection and recognition mechanisms are widely used in many multimedia and security devices. The concept is called face detection and there are significant numbers of studies into face recognition, particularly for image processing and computer vision. However, there remain significant challenges in the existing systems due to limitations behind algorithms. Viola Jones and Cascade Classifier are considered the best algorithms from among existing systems. They can detect faces in unconstrained Crowd Scene with half and full face detection methods. However, limitations of these systems are affecting accuracy and processing time. This project presents a propose solution called VJaC (Viola Jones and Cascade). It is based on the study of current systems, features and limitations. This system considered three main factors, processing time, accuracy and training. These factors are tested on different sample images, and compared with current systems.