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Face Recognition
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We would like to introduce you to some of the principles behind face recognition. Currently there are a several methods to achieve face recognition. Among them we have the neural network approach, the statistical approach - primarily based on histograms, the multiresolutional approach, the information theory approach, and the eigenface approach.
We would be focusing on the Eigenface approach. This method was originally suggested by Alex P. Pentland and Matthew A. Turk from MIT in 1991. This method consist on weighting the difference between a given face image and a mean image, which is obtained by averaging a predefined set of faces. The training set is a group of face images from which the mean face is calculated. Face recognition takes place by linearly projecting the image to a low dimensional image space and weighting the difference with respect to a set of eigenvectors. If the difference (weight) is bellow certain threshold, the image is recognized as a known face; otherwise, the face can be classified as an unknown face, or not a face at all.
Some of the limiting factors of this approach are the background, difference in illumination, imaged head size, and head orientation. To solve some of these problems we could identify the location of the head and zoom until we observe most of the face. We could also set the camera's lighting based on the time of the day.
To learn more about face recognition using eigenfaces visit our tutorial or some of the following pages.