A training set consists of face images from which a set of eigen faces can be generated by performing a mathematical process called principal component analysis(PCA) on a large set of images depicting different human faces .The eigenfaces can be used to represent both existing and new faces we can project a new(mean-subtracted) image on the eigenfaces and thereby record how that new face differs from the mean face .The eigen values associated with each eigenface represent how much the images in the training set vary from the mean image in that direction .We lose information by projecting the image on subset of the eigen vectors ,but we minimize this loss by keeping those eigen faces with the largest eigen values .
In this way each student's input image is matched with the training set image. If a match is found attendance is marked as present else marked as absent for that particular student. This overcomes proxy, identifies punctuality of students and also recognizes lecturers and their punctuality and regularity in handling classes. Administration can know about handling of classes at its chair
To implement e-attendance using eigen face algorithm
During system research researcher studied about the fundamentals about the image processing, face detection techniques and face recognition techniques. Then after analysing them it decided to select appropriate techniques to extract partial face
regions and recognize individuals. The document contains introduction, domain research, system research, project development plan, requirement specification and technical investigation, which contain details about above-mentioned areas.
Finally appropriate image processing techniques, face detection techniques and face recognition techniques were selected and justified along with selection of the project developing methodologies and project developing platforms.
This face recognition system will identify individuals based on characteristics of separate face segmentations and the objectives of the project as follows.
Investigation of unique face features of eye, nose and mouth regions for recognises individuals. When it come to separate face regions there are less unique features that help to identify individuals. Identifying unique features of the individuals has being archiving throughout this project.
Improve capabilities of the detecting features of local segmentations of face It is necessary to find the efficient algorithm to extract features of the face segmentations.
Implement robust, efferent face recognition system based on facts found in the research.