2014 International Symposium on Biometrics and Security Technologies (ISBAST) 73 A General Review of Human Face Detection Including a Study of Neural Networks and Haar Feature-based Cascade Classifier in Face Detection Ali Sharifara, Mohd Shafry Mohd Rahim and Yasaman Anisi Department of Computer Graphics and Multimedia, Faculty of Computing Universiti Teknologi Malaysia 81310, Johor, Malaysia {a.sharifara, shafryr, yasaman.anisi}@gmail.com Abstract—Face detection is an interesting area in research application of computer vision and pattern recognition, especially during the past several years. It is also plays a vital role in surveillance systems which is the first steps in face recognition systems. The high degree of variation in the appearance of human faces causes the face detection as a complex problem in computer vision. The face detection systems aimed to decrease false positive rate and increase the accuracy of detecting face especially in complex background images. The main aim of this paper is to present an up-to-date review of face detection methods including feature-based, appearance-based, knowledge-based and template matching. Also, the study presents the effect of applying Haar-like features along with neural networks. We also conclude this paper with some discussions on how the work can be taken further. Keywords—face detection; feature based face detection; human face detection; haar-like features; neural networks. I. INTRODUCTION Over the last decades, the development of technology has facilitated the improvement of real-time vision modules that interact with individuals. Object detection is one of the computer technologies, which is connected to the image processing and computer vision and it interacts with detecting instances of objects from the specified class, such as human faces, building, tree, car and etc. The objects can be taken from the digital images or video frames. The basic aim of face detection algorithms is to determine whether there is any face in an image or not [1]. In other words, face detection is a task where faces shown on pictures or video are searched for automatically. Face detection is one of the domains in object detection, which many methods have been proposed before and all of them aim to detect face(s) in the given image or real time surveillance systems with different accuracy and false detection rates. Furthermore, most of the researchers also mentioned, which machine learning is their main tool to detect faces in static and video mode. During the past several years, the face detection problem has been given an important attention due to the range of its applications in commerce and law enforcement. Moreover, in recent years a lot of pattern recognition and heuristic based methods have been proposed for detecting human face in images and videos [2]. Face detection is the first stage of many face processing systems, including face recognition, automatic focusing on cameras, automatic face confusion in pictures, pedestrian and driver drowsiness detection in cars, criminal identification, access control, etc [3]. The challenging issue which can be mentioned in face detection is inherent diversity in faces such as shape, texture, colour, got a beard\moustache and/or glasses. Furthermore, the photographing occurrence can cause additional differences such as different lighting conditions, head pose and facial expressions. In addition, most of the face detection algorithms can be extended to recognize other objects such as cars, humans, pedestrians, and etc [4]. II. BACKGROUND Face detection is one of the demanding issues in the image processing and it aims to apply for all feasible appearance variations occurred by changing in illumination, occlusions, facial feature, etc [5]. Furthermore, face detection algorithms have to detect faces which appear with different scale and pose. In the last decade, in spite of all these difficulties, superb progress has been made and many systems have shown remarkable performance. The recent advances of these algorithms have also made important contributions in detecting other objects such as buildings, pedestrians, and cars. Face detection algorithms can tolerate some factors which including posture, existence or lack of structural elements, facial expression, Occlusion, Image orientation, Illumination and the speed and time of computation. In the next section some factors have been verified which can effect on the result of face detection algorithms such as head pose, facial expression, image orientation, Occlusion, and Illumination. 978-1-4799-6444-4/14/$31.00 ©2014 IEEE