目的 针对目前人脸图像美化算法存在的对于细节丰富的眼睛和头发等区域处理过度平滑,美化后的图像整体美化效果较差等问题,提出一种基于肤色分割与平滑人脸图像的美化方法。方法 首先对脸部瑕疵特性,用双指数边缘保护滤波器平滑人脸图像的瑕疵,与此同时很好保持图像边缘信息;再通过利用色度直方图自适应快速检测、修正、分割肤色区域;然后利用拟合高斯羽化皮肤区域生成蒙版,融合平滑图像和原图像,保留图像头发背景等细节信息;最后基于人像美感标准,对皮肤亮度通过拟合log曲线实现快速自适应调整人脸图像亮度,增强眼睛等细节,从而快速实现人脸图像美化方法。结果 通过与其他人像美化算法相比较,在保留边缘方面,该算法更有效地对皮肤边缘上的瑕疵进行平滑,达到更好地美化人脸图像;而在时间复杂度方面,相对于前人的算法,计算速度快12倍,实现快速美化人脸图像。结论 该算法适应能力较强,对大部分人脸图像的脸部瑕疵完美去除的同时达到背景信息不变,肤色美白自然,使整体美化效果显著;尤其是细节丰富的边缘区域平滑适度,具有一定的实用性。

Objective Face image beautification is a widely used technique in multimedia, such as digital cameras, mobile terminals, advertising, and video conferences. However, several defects of face image beautification algorithms still exist today, such as the bad effects on detail-rich eyes and hair areas, as well as the poor visual effects on overall facial beautification images. A fast facial beautification method based on skin color segmentation and smoother face image is proposed in this study to overcome the defects of face beautification. Method First, based on characteristics of the skin's smoothness affecting facial attractiveness, facial blemishes are removed. The biexponential edge-preserving smoother is used to smooth the face image for facial defects and keep the important information of image edge. Second, some background information will be lost during the process of achieving skin smoothness, thereby making the separation of the skin and non-skin area a highly significant process. Then, based on the adaptive hue histogram, rapid detection, correction, and segmentation to the color region are achieved. Third, the mask has been obtained using the Gaussian fitting, which can have a box blur for many times for skin feather. Then, the skin feather is used to fuse the smoothed image and the original image to preserve the details of the face image, such as hair and background. Finally, based on the required white and natural skin as the beauty standard of a portrait, face image brightness is speedily adjusted, other important details are enhanced by fitting the log curve, and facial beautification can be achieved quickly. Result Compared with different methods of face image beautification, we performed qualitative and quantitative evaluation on the same face. After comparing with other facial beautification algorithms, we determined that the proposed algorithm in this study is more effective on the smoothness of edge-skin blemishes and has better ability to beautify the face image in terms of edge preservation; in terms of time complexity, its computing rate is 12 times as fast as the previous algorithms, and it ensures faster beautification of the face image relative to the previous images; in terms of operating methods, the proposed algorithm is simple, considering it only uses an adaptive histogram of chromatic channel to correct and segment skin color region, and it can achieve significant effect in color and non-color regions; and edge connection is more natural. Conclusion We have performed a user study for the effectiveness of a proposed algorithm, beautifying a large number of face images with different genders, ages, poses, and backgrounds from the Internet. Experimental results show that the proposed algorithm has strong adaptive capacity because it can perfectly remove facial defects of most face images and it can simultaneously keep the background information unchanged, making the skin white and natural. The effectiveness of overall beautification is significant. In general, with its good beautification effect, the proposed algorithm obtains consistent high praise from many users. Specifically, it can moderately smooth the edges of the areas with many details, leaving no traces of an artificial process. The proposed algorithm with fast beautification of face image has a wide range of practicality.