Face recognition with eigenfaces

Develops an approach to face recognition using eigenfaces, focusing on the effects of the eigenface used to represent a human face under several environment conditions. The authors first derive some computational feasible formula to find the eigenfaces, then investigate the relationship of mean absolute error between original face images and reconstructed images under various conditions such as face size, lighting and head orientation changes. The experimental results show that a large number of eigenfaces are not necessary to describe an individual face and only about 80 eigenfaces are sufficient for a large size set of face images. Gaussian smoothing can minimize the error under the same conditions. Finally, a face recognition system with eigenfaces and backpropagation neural network is implemented.<
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