Human Face Recognition




                   In the urgent obligation to protect the health and property of inhabitants is in need to quickly find appropriate methods. Biometrics is one of the general technical method that is almost immediately claimed. An analysis of the behavioral characteristics can not surely identify a terrorist who lives undiscovered between the inhabitants of a nation. Therefore to fight against terrorism and for the safety measures of citizens biometrics is applicable throughout the world. Despite the potential benefits of this technology, many citizens are concerned that their privacy will be invaded. Some fear that it could lead to a “total surveillance society,” with the government and other authorities having the ability to know where we are, and what we are doing, at all times.            

                  We present an approach to the detection and identification of human faces and describe a working , near-real time face recognition system which tracks a subject’s head and then recognize the person by comparing the characteristics of the face to those of known individuals. Our approach is to treat face as a 2-D set of characteristics , taking advantage of that normally a human face is upright and so it can be described  as a set of  2-D characteristic views. The face images are projected onto a feature space (“ face space“) That best encodes the variation among known face images. This face spaces are called “eigen faces”, which are the eigen vectors of faces. They do not necessarily correspond to eyes, faces, noses. The framework gives the ability to learn to recognize new faces in an unsupervised manner.

In our project , we  will discuss how to construct a face recognition program .We treat  3-D human faces as 2-D image , then we take eigen faces of the images  and compare the images by CROSS-CORRELATION method. Though  there are number of algorithms for face recognition but in this texture we will discuss on cross-correlation method by using MATLAB.

                  Face recognition is a task humans perform remarkably easily and successfully. This apparent simplicity was shown to be dangerously misleading as the automatic face recognition seems to be a problem that is still far from  solved. In spite of more than 20 years of extensive research, large number of papers published in journals and  conferences dedicated to this area, we still cannot claim that artificial systems can measure to human performance.  Automatic face recognition is intricate primarily because of difficult imaging conditions (lighting and viewpoint changes  induced by body movement) and because of various other effects like aging, facial expressions, occlusions etc.  Researchers from computer vision, image analysis and processing, pattern recognition, machine learning and other  areas are working jointly, motivated largely by a number of possible practical applications. The goal of this book is to  give a clear picture of the current state-of-the-art in the field of automatic face recognition across three main areas  of interest: biometrics, cognitive models and human-computer interaction. Face recognition has an important  advantage over other biometric technologies - it is a nonintrusive and easy to use method. As such, it became one of  three identification methods used in e-passports and a biometric of choice for many other security applications.  Cognitive and perception models constitute an important platform for interdisciplinary research, connecting scientists  from seemingly incompatible areas and enabling them to exchange methodologies and results on a common problem.  Evidence from  neurobiological ,psychological, perceptual and cognitive experiments provide potentially useful insights  into how our visual system codes, stores and recognizes faces. These insights can then be connected to artificial  solutions. On the other hand, it is generally believed that the success or failure of automatic face recognition  systems  might inform cognitive and perception science community about which models have the potential to be  candidat es for those used by humans. Making robots and computers more "human" (through human-computer  interaction ) will improve the quality of human-robot co-existence in the same space and thus alleviate their adoption  into our everyday lives. In order to achieve this, robots must be able to identify faces, expressions and emotions while  interacting with humans. Hopefully, this book will serve as a handbook for students, researchers and practi tioners in the area of automatic (computer) face recognition and inspire some future research ideas by identifying  potential research directions. The book consists of 28 chapters, each focusing on a certain aspect of the problem.  Within every chapter the reader will be given an overview of background information on the subject at hand and in   many cases a description of the authors' original proposed solution. The chapters in this book are sorted  alphabetically, according to the first author's surname. They should give the reader a general idea where the current  research efforts are heading, both within the face recognition area itself and in interdisciplinary approaches .


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