Face Recognition through Different Facial Expressions for Women Security: A Survey Soumya Xavier #1 Master Of Technology, Computer Science And Engineering Sahrdaya college of Engineering And Technology Calicut University, Kerala, Jasmine Jolly #2 Assistant Professor, Computer Science And Engineering Sahrdaya college of Engineering And Technology Calicut University, Kerala Vince Paul #3 Assistant Professor, Computer Science And Engineering Sahrdaya college of Engineering And Technology Calicut University, Kerala Abstract— Face is an essential part of our daily life. It is a complex multidimensional structure and needs a good computing technique for recognition. Face recognition has been a fast growing, challenging and interesting area in real time applications. A large number of face recognition algorithms have been developed in last decades. In this paper an attempt is made to review a wide range of methods used for face recognition comprehensively. While using automatic system for face recognition, computers are easily confused by changes in illumination, variation in poses and change in angles of faces. A numerous techniques are being used for security and authentication purposes which includes areas in detective agencies and military purpose. These surveys give the existing methods in automatic face recognition and formulate the way to still increase the performance. Keywords— Face Recognition, Illumination, Authentication, Security I. INTRODUCTION Face recognition is an important part of the capability of human perception system and is a routine task for humans, while building a similar computational model of face recognition. The computational model not only contribute to theoretical insights but also to many practical applications like automated crowd surveillance, access control, design of human computer interface (HCI), content based image database management, criminal identification and so on. Developed in the 1960s, the first semi-automated system for face recognition required the administrator to locate features ( such as eyes, ears, nose, and mouth) on the photographs before it calculated distances and ratios to a common reference point, which were then compared to reference data. In the 1970s, Goldstein, Armon, and Lesk used 21 specific subjective markers such as hair color and lip thickness to automate the recognition. The problem with both of these early solutions was that the measurements and locations were manually computed. The face recognition problem can be divided into two main stages: face verification (or authentication), and face identification (or recognition).The detection stage is the first stage; it includes identifying and locating a face in an image. The recognition stage is the second stage; it includes feature extraction, where important information for the discrimination is saved and the matching where the recognition result is given aid of a face database. II. LITERATURE SURVEY A .Local approach The local approach was the foremost strategy used by early face recognition systems. The hypothesis behind this approach is that face recognition system is completely damaged when facial features are edited or spatially reorganized [8]. In fact, before performing face recognition, the local features such as eyes, nose and mouth are detected. Then, facial features positions and local geometric and/or appearance statistics are supplied for a structural classifier [23]. Methods within this approach may be classified into mainly classes: Geometric methods ,and Template based methods Correlation based methods, Model based methods. Geometric Feature Based Methods: The geometric feature based approaches are the earliest approaches to face recognition and detection. In these systems, the significant facial features are detected and the distances among them as well as other geometric characteristic are combined in a feature vector that is used to represent the face. To recognize a face, first the feature vector of the test image and of the image in the database is obtained. Second, a similarity measure between these vectors, most often a minimum distance criterion, is used to determine the identity of the face. In the geometric methods, some heuristic rules that involve angles, distances and areas are used to define the distribution of the facial features. It computes the distance and angles between eye corners, the width of the head, the distance between the eyes and from eyes to the mouth, etc. [26]. In [7], the authors defined facial features as points in one form for which objectively meaningful and reproducible biological counterparts exist Soumya Xavier et al | IJCSET(www.ijcset.net) | January 2016 | Vol 6, Issue 1, 43-47 43