Face Recognition System with GUI Using Digital Image Processing
K. Sharath Reddy1, M.C. Sankalp2, K. Pradeep Kumar3, K.P. Shashidhar4

1K. Sharath Reddy, Dept. of Electronics and Communication Engineering, MLRIT, Dundigal, Hyderabad Telangana, India.
2M.C. Sankalp, Dept. of Electronics and Communication Engineering, MLRIT, Dundigal Hyderabad Telangana, India.
3K. Pradeep Kumar, Dept. of Electronics and Communication Engineering, MLRIT, Dundigal Hyderabad Telangana, India.
4K.P. Shashidhar, Dept. of Electronics and Communication Engineering, MLRIT, Dundigal Hyderabad Telangana, India.
4Vibha Ravichandran, Department of Computer, MIT College of Engineering, Savitribai Phule Pune University Pune, India.
Manuscript received on December 02, 2014. | Revised Manuscript Received on December 18, 2014. | Manuscript published on December 20, 2014. | PP: 7-13 | Volume-3, Issue-1, December 2014. 
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© The Authors. Published By: Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: A facial recognition system is a computer application for automatically identifying or verifying a person from a digital image or a video frame from a video source. One of the ways to do this is by comparing selected facial features from the image and a facial database. It is typically used in security systems and can be compared to other biometrics such as fingerprint or eye iris recognition systems. An approach to the detection and identification of human faces is presented, and a working, near-real-time face recognition system which tracks a subject’s head and then recognizes the person by comparing characteristics of the face to those of known individuals is described. This approach treats face recognition as a twodimensional recognition problem, taking advantage of the fact that faces are normally upright and thus may be described by a small set of 2-D characteristic views. Face images are projected onto a feature space (`face space’) that best encodes the variation among known face images. The face space is defined by the `Eigen faces’, which are the eigenvectors of the set of faces; they do not necessarily correspond to isolated features such as eyes, ears, and noses. The framework provides the ability to learn to recognize new faces in an unsupervised manner. In this report we discuss about the feature based recognition and Eigen face method for facial analysis. In feature based facial recognition method the importance is given to the facial features, whereas the Eigen face method gives preference to the face. By combining both the above methods we obtain.
Keywords: “Feature Based Eigen face Method” for facial recognition.