Discrimination among individuals using orthogonal transformed face images

We investigated the discrimination among individuals by comparing the individual face image which applied an orthogonal transform with the reference images. Image recognition is affected by the conditions of the image, like the distance from the camera to a face or the brightness of the illumination. Therefore a normalizing method which adjusts to the changes of the distance and brightness is needed for correct recognition. Adjustment of the distance was carried out by enlarging/reducing the image based on the positions of the eyes, nose and mouth. The orthogonal transform is suitable for extracting the characteristic parts of an image. Moreover, it is not affected by the changes of the brightness of the illumination. We applied five kinds of transforms (discrete Fourier, Walsh-Hadamard, Haar, discrete cosine and wavelet) to the human face image. For each transform, two types of pattern matching methods were examined. One used the whole transformed image with no change, and the other used the weighted images. Weighting is performed to extract only a small part which includes individual characteristics from the image. We compared the discrimination rates for the five transforms. As a result, it has been shown that the weightings was effective to improve the discrimination rate. For all five transforms, the discrimination rate was improved by 5%-40% using weighting. In addition only small parts of the transformed image were useful for discrimination. When the discrete Fourier transform was used, the highest discrimination rate of 96.8% was obtained.