Abstract-Facial retouching is widely used in media and entertainment industry. Professional software usually require a minimum level of user expertise to achieve the desirable results. In this paper, we present an algorithm to detect facial wrinkles/imperfection. We believe that any such algorithm would be amenable to facial retouching applications. The detection of wrinkles/imperfections can allow these skin features to be processed differently than the surrounding skin without much user interaction. For detection, Gabor filter responses along with texture orientation field are used as image features. A bimodal Gaussian mixture model (GMM) represents distributions of Gabor features of normal skin versus skin imperfections. Then, a Markov random field model is used to incorporate the spatial relationships among neighboring pixels for their GMM distributions and texture orientations. An expectation-maximization algorithm then classifies skin versus skin wrinkles/imperfections. Once detected automatically, wrinkles/imperfections are removed completely instead of being blended or blurred. We propose an exemplar-based constrained texture synthesis algorithm to inpaint irregularly shaped gaps left by the removal of detected wrinkles/imperfections. We present results conducted on images downloaded from the Internet to show the efficacy of our algorithms. Index Terms-Facial wrinkles, skin imperfections, Markov random field, Gaussian mixture model, Gabor features, texture orientation fields.
The exemplar-based constrained texture synthesis algorithm, as the researchers conclude is the most ideal tool for in-painting irregularly shaped gaps that are left behind in the form of scars of removed wrinkles REF .
Detection and Inpainting of Facial Wrinkles Using Texture Orientation Fields and Markov Random Field Modeling
IEEE Transactions on Image Processing
IEEE Transactions on Image Processing
Medicine Computer Science Mathematics