LAUSR

This paper proposes a novel rotation-invariant multi-spectral facial recognition approach (RIMFRA) by using orthogonal polynomials. In the first step, a rotation, illumination and noise invariant local descriptor (RinLd) is proposed to represent the texture patterns of a face image. Color channels of the images embodies non-trivial information about the characteristic of the image. Hence, the local descriptor matrices are extracted among the color channels. The corresponding new descriptor matrices for the red, green and blue channels of the image are extracted. Afterwards, co-occurrence matrices are obtained from the six combinations of the corresponding color channel descriptor matrices, that are red-red, blue-blue, green-green, red-blue, green-blue and red-green. Finally, these matrices are decomposed by using the orthogonal polynomials to achieve a more reliable and characteristic pattern extraction. The coefficients obtained as a result of the decomposition process are used as the ultimate features for the classification of the images. Extensive simulations are conducted over benchmark datasets. As presented by the simulation results, the ultimate features yield very high discriminating performance as well as providing resistance to rotation and illumination variations.