Dimensional Blurring of Large Face Image Based on Structural Similarity

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Thitiporn Lertrusdachakul
Kasem Thiptarajan
Kanakarn Ruxpaitoon
Kulwadee Somboonviwat


     Face masking is widely used in print media, digital media and various online images for privacy and security protection. This research proposes an innovative face-blurring method focusing on large face blurring in large image size which usually requires a high degree of blur level to anonymize face. This great blur causes the face to be flat or very smooth and lack of visual dimension. Therefore, this research exploits the local structural similarity between large blurred and original images with Gaussian filter and contrast adjustment to adaptively create an opacity map for smoothly and appropriately blurring of face components. The details of main components are then very blurry and difficult to recognize personal identity while other parts of the face are less blurred. This adaptively face smoothing can improve a sense of dimensional perception and help to visually anonymize the portrait more natural resulting in the higher average value of structural similarity to the original image. The proposed method can be further applied to an aesthetically image censoring.


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