Fig. 7:

Additional test results in eyeglass detection and localization by removal. The difference map (Column 3 for StarGan; Column 6 for Fixed-Point GAN) shows the absolute difference between the input (Column 1) and output (Column 2 for StarGAN; Column 5 for Fixed-Point GAN). Applying the k-means clustering algorithm on the difference map yields a localization map, which is then superimposed on the original image (Column 4 for StarGAN; Column 7 for Fixed-Point GAN), showing both StarGAN and Fixed-Point GAN attempt to remove eyeglasses. However, the former leaves noticeable white “inks” along eyeglass frames (Rows 1 and 4, Column 2), while our method better preserves the face color. Removing sunglasses (Rows 5–9) has proven to be challenging: both methods suffer from partial removal and artifacts. Nevertheless, Fixed-Point GAN tends to recover the face under the glasses and frames, but StarGAN only changes regions around the frames. More importantly, our method can “insert” eyes at proper positions, as revealed in the difference maps (Rows 5–9, Column 6), while StarGAN can hardly do so. To better visualize the subtle changes for negative samples (Column 8), instead of the absolute difference, we show the difference directly, where the gray color (i.e., 0) means “no change”. In this way, it can be observed more easily that StarGAN does some unnecessary small changes on hair (Rows 7 and 9, Column 10) and eyes (Rows 7 and 10, Column 10), while Fixed-Point GAN generates smooth gray images (i.e., close to 0 everywhere; Column 12). Please note that the CelebA Dataset currently does not have ground truth on the location and segmentation of glasses; therefore, a quantitative performance evaluation of eyeglass localization cannot be conducted. However, our quantitative performance evaluations of brain lesion localization and pulmonary embolism localization are included in Sec. 4.