Abstract
Here the faces are vertically oriented frontal view with wide expression change. They are extracted from the images by the face detection code first. It will eliminates background influence as much as possible. The face space is described by a set of eigenfaces. Each face is efficiently represented by its projection onto the space expanded by the eigenfaces and has a new descriptor. Neural networks are used to recognize the face through learning correct classification of these new descriptors. A real-time system can be created which combines the face detection and recognition techniques. The images are taken using a camera. The face in the image is then detected and it is recognized. This face database can be easily expanded to accommodate more individuals.
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