De-disguise Faces for Accurate Disguised Faces Recognition

In recent years, face recognition has become an important and reliable application of deep learning used in many industries on a global scale, reaching from smartphone user identification to border security. Despite recent advances in the design of loss functions working with angular margins, disguise and impersonation still pose a challenge to face recognition systems. Even unintentional changes in a subject's appearance can significantly reduce the accuracy of correctly classifying an individual as genuine or imposter. Although methods like ArcFace increase the detection rate of imposters, they do not attempt to visually remove disguise which will be the main focus of this work. Providing a de-disguised picture enhances the human ability to correctly identify a subject as genuine or imposter but can also increase the discriminative power of the state of the art classification models. Throughout this work, the method of de-disguising disguised faces will be derived from well-known concepts in the field of deep learning and evaluated in comparison to classic and cutting edge methods to better understand the promises and problems of this approach.