Evaluation of effectiveness of eye massage therapy via classification of periocular images.

This paper proposes a method to evaluate the effectiveness of the eye message therapy. The existing methods are via the diagnoses conducted by the medical professions based on the measurements acquired by the optical instruments. However, this approach is very expensive. To address this issue, this paper performs the classification between the periocular images taken before performing the eye massage therapy and those after performing the eye massage therapy to address the above difficulty. First, the median filtering is used to suppress the solitary point noise with preserving the edges of the image without causing the significant blurring. Then, the Canny operator is employed to accurately locate the edges. Next, the circle Hough transform (CHT) is used for performing the iris segmentation. Finally, various classifiers are used to perform the classification. The computer numerical simulation results show that our proposed method can achieve the high classification accuracies. This implies that there is a significant difference on the iris before performing the eye massage therapy and after performing the eye massage therapy. In addition, the comparisons with the state of art Daugman method have been performed. It is found that the classification performance achieved by the CHT based method is better than those achieved by the Daugman method.