Detecting Occluded Faces in Unconstrained Crowd Digital Pictures S. Janahiram 1 , Abeer Alsadoon 1 , P. W. C. Prasad 1 , A. M. S. Rahma 2 , A. Elchouemi 3 , and SMN Arosha Senanayake 4 1 School of Computing and Mathematics, Charles Sturt University, Sydney, Australia 2 Computer Science Department, University of Technology, Baghdad, Iraq 3 Walden University, USA 4 Faculty of Science, Universiti Brunei Darussalam rsajee@live.com; aalsadoon@uaeu.ac.ae; cwithana@studygroup.com; amr.elchouemi@adec.ac.ae; monem.rahma@yahoo.com; arosha.senanayake@ubd.edu.bn Abstract 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. Keywords- Face Detection; Unconstrained Crowd Digital Pictures; Face Recognition I. INTRODUCTION In recent years, face detection and recognition systems have been widely used in every aspect of life. The initial stage of the face recognition system is face detection in the images. This concept detects the face of a person and the location in the image to verify the information, working through image processing and computer vision. The function of the system is described as a pre-processing stage of face detection. However, due to the fact that faces may not be fully focused on the camera or be partially hidden due to crowed scene, difficulties are creating in detecting the face accurately. The main challenge seems to come from differences in facial appearance. To overcome this, current solutions use an Adaboost Machine Learning (AML) approach and Viola-Jones (VJ) algorithm to detect the faces. The main feature of this solution is that the algorithm includes a skin color detection module that allows it to exactly focus on faces through image skin color to compare and identify the images [1]. This solution has improved the accuracy and performance of face detection. That means, the system runs separate algorithms to detect full and half faces which improves the capability of the system. The primary objective of this solution is to detect several types of objects in the image, namely holistic faces, half faces, and skin color. Despite the success of this solution, however, there are challenges as blurred images cannot be clearly identified and the response time is greater. To overcome these limitations, our proposed project focuses on implementing the VJaC (Viola Jones and Cascade). This paper is organized as follows: Section I introduces the project, and section II reviews existing research into face detection. This is followed by a description of the methodology used in the proposed solution, in section III, together with an analysis and overview of implementation strategies. Results of the current research into the proposed solution are discussed in section IV under results and discussions, while the conclusion is covered under section V, which is also considers possible future work. II. RELATED WORK A current solution to face detection in unconstrained crowd scene images was proposed by [1] based on a machine learning approach. The main features of this solution are that several types of objects in images can be detected, including holistic and half faces. Based on testing, the accuracy rate of the solution is 95%. This is higher than for other current solutions. Xinjun, Hongqiao and Xin [2], identified the Modified Skin-color Model as detecting faces in the images although they are affected by different lighting environments. In the same way, Sharifara, Mohd, Mohd and Anisi [3] proposed Neural Networks and Haar Feature-based Cascade Classifier in Face Detection. This solution is tackling the issues caused by different expressions and appearances of faces in the image. Reney and Tripaathi [4] proposed work whereby the algorithm can detect the face and emotion of the face. Another algorithm for Face Detection using Multi- modal Features was proposed by Lee, Kim, Kim and Lee [5]. Kumar and Bindu [6] proposed an algorithm for Skin Region Detection based on a skin illumination compensation model for efficient face detection. Likewise Xingjing, Chunmei and Yongxia [7] produced an algorithm for detection and tracking a Partially Occluded Face. The goal of this solution is that the system detects the faces in an image or video and compares the similarity between the faces which has been detected by the system. Sun, Zhang, Zhang, Chen and Lv [8]