Then, the face restoration network (FaceR) restores the retouched image based on the retouching label predicted by the facial retouching detector. Therefore, the development of facial retouching restoration technology holds significant practical significance and application prospects. The application of this technology will simplify the identity verification process, enhance user experience, help reduce misjudgments, and improve the reliability of the system. Therefore, if we could adopt more advanced facial retouching restoration technology, we could directly use the retouched photo for identity verification without requiring users to upload it again. Although this method can help improve the accuracy and reliability of identity verification, it requires users to upload photos multiple times, increasing the complexity of the operation and inconvenience to the user experience. If the detection result indicates that the photo has been retouched, the system can reject the photo and request the user to upload a new one. Suppose in an identity verification system, we employ facial retouching detection technology to determine whether the photo uploaded by the user has undergone retouching processing. However, these technologies currently only detect whether a photo has been retouched, without being able to determine the specific retouching methods and their degrees, which to a certain extent limits their applicability. In the current research and technological development, the primary objective of facial retouching detection technology is to classify facial photographs to determine whether they have undergone retouching processing (Rathgeb et al. Similarly, in online social platforms involving interpersonal trust, excessively retouched photos may violate the principles of honest communication, leading to social issues such as identity fraud. , 2012) points out the impact of makeup operations on automatic facial recognition. However, if the provided facial images have been retouched, they may pose a threat to the system’s accuracy, resulting in erroneous identity confirmation and introducing security risks. Specifically, in fields such as judicial crime investigation, financial services, and security supervision, facial recognition-based identity verification systems are widely used.

However, when retouched photos are applied in contexts requiring authenticity and accuracy, issues arise. Undeniably, photo editing technology plays a positive role in satisfying people’s pursuit of beauty and shaping personal brands. From social media to personal avatars, features such as enlarging eyes, smoothing skin, and slimming faces make facial retouching extremely convenient, turning retouched facial photos into a cultural phenomenon. In contemporary society, we witness the increasing prevalence of photo editing tools and techniques, enabling virtually anyone to digitally construct a version of themselves closer to their ideals with a simple click or swipe (Rathgeb et al. Extensive experiments demonstrate the effectiveness of our framework and each module. Finally, H-AdaIN is applied to address the issue of color shift arising from diffusion models. Then FaceR restores the retouched image based on the predicted retouching label. Firstly, the facial retouching detector predicts a retouching label containing three integers, indicating the retouching methods and their corresponding degrees. To tackle the problem of facial retouching restoration, we propose a framework, dubbed Face2Face, which consists of three components: a facial retouching detector, an image restoration model named FaceR, and a color correction module called Hierarchical Adaptive Instance Normalization (H-AdaIN). To address this issue, some work has proposed makeup removal methods, but they still lack the ability to restore images involving geometric deformations caused by retouching. This may pose challenges to the reliability of identity verification systems and social media, and even lead to online fraud. By altering facial images, users can easily create deceptive images, leading to the dissemination of false information. This poses challenges for fields that place high demands on the authenticity of photographs, such as identity verification and social media. With the popularity of social media platforms such as Instagram and TikTok, and the widespread availability and convenience of retouching tools, an increasing number of individuals are utilizing these tools to beautify their facial photographs.

Face2face software free download