Abstract: This paper focuses on the challenging task of counting the number of people in digital still images, which has important applications in many fields such as security, management, education, and commerce. This paper proposes a system that is based on the Viola-Jones face detection methods. This system consists of two parts: a) face detection and b) counting the detected faces. In the face detection part, the Viola-Jones (LBP and CART feature extraction) algorithm is applied to the input image. In the counting part, the detected faces are counted to estimate the number of people in the given image. The Viola-Jones algorithm is applied using 133 images from the People Image Groups dataset, and the best precision achieved is 96.9%. Overall, this paper presents a promising system for accurately counting the number of people in digital static images using a simple and costeffective approach

Counting the number of people in many estimation systems, such as still images or video frames, is a buoyant research area that is challenging in the field of computer vision. It plays a considerable role in a variety of applications, such as security, management, education, and commerce. The purpose of this paper is to suggest a system to estimate the number of people in digital still images based on the Face Detection method. This system composed of two parts: face detection and counting of detected faces. In the detection step, the Skin Color Face Detection method was applied on the input of a digital still image. In the counting part, the obtained detected faces by the Skin Color Face Detection method have counted to estimate the number of people in an input color image with simple software and simple low-cost hardware. The skin color face detection algorithm was tested using 133 images from the People Image Groups dataset, which contains about 2573 color images of people, to test the proposed system. based on the obtained results, the best precision achieved of the proposed Skin Color face detection algorithm was 85%