Face recognition in e-attendance

Now-a-day face recognition phenomenon gains lot of attention in society of network and multimedia information access. Areas such as network security, content indexing and retrieval and video compression benefits from face recognition because faces of people are main attention in video. In network access control via face recognition it is difficult for hackers virtually impossible to steal one’s password. It is very user friendly application, it increase the human computer interaction. A training set consists of images of faces from which a set of eigenfaces can be formed by performing mathematical calculations called principal component analysis on a large set images depicting different human faces. The eigen faces can be used to represent both existing and new faces we can project a new 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.

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