A.Swaminathan et al , International Journal of Computer Science and Mobile Computing, Vol.3 Issue.1, January- 2014, pg. 233-243 © 2014, IJCSMCAll Rights Reserved 233 Available Online at www.ijcsmc.com International Journal of Computer Science and Mobile Computing A Monthly Journal of Computer Science and Information Technology ISSN 2320–088X IJCSMC, Vol. 3, Issue. 1, January 2014, pg.233 – 243 REVIEW ARTICLE A Review of Numerous Facial Recognition Techniques in Image Processing A.Swaminathan 1 , N.Kumar 2 , M.Ramesh Kumar 3 1 M.E Student, 2,3 Asst Professor, 1, 2, 3 Department of Computer Science & Engineering, 1, 2, 3 Veltech Multitech Dr.Rangarajan Dr.Sakunthala Engineering College, Chennai, India tamilveerans@gmail.com 1 , nkvsc@gmail.com 2 , maestro.ramesh@gmail.com 3 Abstract: Recognizing faces in images is an emerging trend of research in image processing streams. There were various systems proposed in this stream. Human emotions and intentions are communicated more often by changes in one or two discrete facial features. Given a single image, the goal of face detection is to identify all image regions which contain a face regardless of its three-dimensional position, orientation, and lighting conditions. Such a problem is challenging because faces are no rigid and have a high degree of variability in size, shape, colour, and texture. Numerous techniques have been developed to detect faces in a single image, and the purpose of this paper is to categorize and evaluate these algorithms. We also discuss relevant issues such as data collection, evaluation metrics, and benchmarking. After analysing these algorithms and identifying their limitations, we conclude with several promising directions for future research. Keywords—Face detection; face recognition; object recognition; view-based recognition; statistical pattern recognition; machine learning I.INTRODUCTION With the ubiquity of new information technology and media, more effective and friendly methods for human computer interaction (HCI) are being developed which do not rely on traditional devices such as keyboards, mice, and displays. Furthermore, the ever decreasing price/performance ratio of computing coupled with recent decreases in video image acquisition cost imply that computer vision systems can be deployed in desktop and embedded systems [1-3]. The rapidly expanding research in face processing is based on the premise that information about a user’s identity, state, and intent can be extracted from images, and that computers can then react accordingly, e.g., by observing a person’s facial expression .In the last five years, face and facial expression recognition have attracted much attention though they have been studied for more than 20 years by psychophysicists, neuroscientists, and engineers. Many research demonstrations and commercial applications have been developed from these efforts. A first step of any face processing system is detecting the locations in images where faces are present. However, face detection from a single image is a challenging task because of variability in scale, location, orientation (up-right, rotated), and pose (frontal, profile). Facial expression, occlusion, and lighting conditions also change the overall appearance of faces. We now give a definition of face detection: Given an arbitrary image, the goal of face detection is to determine whether or not there are any faces in the image and, if present, return the image location and extent of each face. The challenges associated with face detection can be attributed to Pose, Presence or absence of structural components, Facial expression, Occlusion, Image orientation and Imaging conditions. Face detection also provides interesting challenges to the underlying pattern classification and learning techniques. When a raw or filtered image is considered as input to