Light variations from different directions on the face images cause severe performance degradation in face recognition system. These variations should be nullified or suppressed so that the recognition performance can be improved. Here, the objective is to develop a robust and pragmatic method which can compensate the effect of uncontrolled light incident from different directions on the person’s face. To normalize the effect of unpredictable illumination variations from face images, a novel illumination–normalization method is proposed by utilizing the fractional discrete cosine transform (Fr-DCT) and a nonlinear modifier. Fr-DCT provides a generalized α-domain in which illumination variation’s effect can be better processed. The proposed method adaptively selects and processes the low-frequency α-domain coefficients depending upon the light variations incident on the face image. The illumination-normalized images are classified with the help of a non-iterative classifier. The parameters of the proposed method are optimized with the help of genetic algorithm. The proposed method is tested over Extended YALE B, AR, CMU PIE and YALE face databases. Very promising results have been achieved with percentage error rate zero for CMU PIE and subset 3 of Extended YALE B, 0.38% for subset 4 and 0.55% for subset 5. On AR database, error rate is 0.5% when only illumination-varying images are considered for testing. The comparison of the results of the proposed method with those of the existing state-of-the-art methods clearly establishes the superiority of the proposed method.

面部图像上不同方向的光线变化会导致面部识别系统的性能严重下降。这些变化应被消除或抑制，以便可以提高识别性能。在此，目的是开发一种鲁棒且实用的方法，该方法可以补偿从不同方向入射到人的面部上的不受控制的光的影响。为了归一化面部图像中不可预测的照明变化的影响，提出了一种利用分数离散余弦变换（Fr-DCT）和非线性修改器的新型照明归一化方法。Fr-DCT提供了一个广义的α域，可以更好地处理照明变化的影响。所提出的方法自适应地选择和处理低频α区域系数取决于入射在面部图像上的光变化。借助非迭代分类器对照明标准化图像进行分类。该算法的参数在遗传算法的帮助下进行了优化。在扩展的YALE B，AR，CMU PIE和YALE人脸数据库上对提出的方法进行了测试。对于CMU PIE和扩展YALE B的子集3，对于子集4，子集3为0.38％，对于子集5为0.55％，错误率为零，已经获得了非常有希望的结果。在AR数据库中，当仅照度变化的图像是时，错误率为0.5％考虑进行测试。将所提出的方法的结果与现有的最新技术方法的结果进行比较，显然可以确定所提出的方法的优越性。