Filter-based face recognition under varying illumination

In this study, the authors develop a new algorithm for face recognition with varying lighting conditions. Their method first performs low-pass and high-pass filtering to the face image, and then takes the ratio between the two filtered images. The authors take the arctangent to the ratio and use these features to classify an unknown face image. In addition, their method works for any combination of low-pass and high-pass filters. The authors studied two sets of the low-pass and high-pass filters in their experiments and their results are better than gradient faces, Weber faces, and self-quotient images (SQIs) in the noisy environment, no matter denoising or no denoising is performed to the noisy face images for the CMU-PIE and Extended Yale-B databases. Nevertheless, the SQI is best for the CAS-PEAL face database in authors' experiments. The SQI takes a convolution operation with a low-pass filter. The implementation of SQI may have chosen a more suitable low-pass filter for the CAS-PEAL face database, but not for the YALE and CMU-PIE face databases. This may be the main reason why SQI outperformed their proposed method in this study for the CAS-PEAL face database. Nevertheless, the SQI is many times slower than their proposed method.