Abstract—Authentication is a significant issue in system control in computer based communication. Human face recognition is an important branch of biometric verification and has been widely used in many applications, such as video monitor system, human-computer interaction, and door control system and network security. This paper describes a method for Student’s Attendance System which will integrate with the face recognition technology using Personal Component Analysis (PCA) algorithm. The system will record the attendance of the students in class room environment automatically and it will provide the facilities to the faculty to access the information of the students easily by maintaining a log for clock-in and clock-out time. Index Terms—Face recognition system, automatic attendance, authentication, bio-metric, PCA. I. INTRODUCTION Face recognition is as old as computer vision, both because of the practical importance of the topic and theoretical interest from cognitive scientists. Despite the fact that other methods of identification (such as fingerprints, or iris scans) can be more accurate, face recognition has always remains a major focus of research because of its non- invasive nature and because it is people's primary method of person identification. Face recognition technology is gradually evolving to a universal biometric solution since it requires virtually zero effort from the user end while compared with other biometric options. Biometric face recognition is basically used in three main domains: time attendance systems and employee management; visitor management systems; and last but not the least authorization systems and access control systems. Traditionally, student’s attendances are taken manually by using attendance sheet given by the faculty members in class, which is a time consuming event. Moreover, it is very difficult to verify one by one student in a large classroom environment with distributed branches whether the authenticated students are actually responding or not. The present authors demonstrate in this paper how face recognition can be used for an effective attendance system to automatically record the presence of an enrolled individual within the respective venue. Proposed system also maintains a log file to keep records of the entry of every individual with respect to a universal system time. Manuscript received March 8, 2012; revised May 14, 2012. The authors are with the Computer Science and Engineering Department, National Institute of Technology, Agartala, India (e-mail: nirmalya.kar@gmail.com, mkdb06@gmail.com, ashim.nita@gmail.com, dwijen.rudrapal@gmail.com). A. Background and Related Work The first attempts to use face recognition began in the 1960’s with a semi-automated system. Marks were made on photographs to locate the major features; it used features such as eyes, ears, noses, and mouths. Then distances and ratios were computed from these marks to a common reference point and compared to reference data. In the early 1970’s Goldstein, Harmon and Lesk [2] created a system of 21 subjective markers such as hair colour and lip thickness. This proved even harder to automate due to the subjective nature of many of the measurements still made completely by hand. Fisher and Elschlagerb [3] approaches to measure different pieces of the face and mapped them all onto a global template, which was found that these features do not contain enough unique data to represent an adult face. Another approach is the Connectionist approach [4], which seeks to classify the human face using a combination of both range of gestures and a set of identifying markers. This is usually implemented using 2-dimensional pattern recognition and neural net principles. Most of the time this approach requires a huge number of training faces to achieve decent accuracy; for that reason it has yet to be implemented on a large scale. The first fully automated system [5] to be developed utilized very general pattern recognition. It compared faces to a generic face model of expected features and created a series of patters for an image relative to this model. This approach is mainly statistical and relies on histograms and the gray scale value. II. SYSTEM OVERVIEW The present authors used the eigenface approach for face recognition which was introduced by Kirby and Sirovich in 1988 at Brown University. The method works by analyzing face images and computing eigenface [8] which are faces composed of eigenvectors. The comparison of eigenface is used to identify the presence of a face and its identity. There is a five step process involved with the system developed by Turk and Pentland [1]. First, the system needs to be initialized by feeding it a set of training images of faces. This is used to define the face space which is set of images that are face like. Next, when a face is encountered it calculates an eigenface for it. By comparing it with known faces and using some statistical analysis it can be determined whether the image presented is a face at all. Then, if an image is determined to be a face the system will determine whether it knows the identity of it or not. The optional final step is that if an unknown face is seen repeatedly, the system can learn to recognize it. Study of Implementing Automated Attendance System Using Face Recognition Technique Nirmalya Kar, Mrinal Kanti Debbarma, Ashim Saha, and Dwijen Rudra Pal International Journal of Computer and Communication Engineering, Vol. 1, No. 2, July 2012 100