Abstract:Automatic makeup refers to the editing and synthesis of face makeup through computer algorithms. It belongs to the field of face image analysis, and plays an important role in interactive entertainment applications, image and video editing, and face recognition. However, as a face editing problem, it is still difficult to ensure that the editing result of the image is natural and satisfies the editing requirements. Makeup still has some difficulties such as precisely controlling the editing area is hard, the image consistency before and after editing is poor, and the image quality is insufficient. In response to these difficulties, this study innovatively proposes a mask-controlled automatic makeup generative adversarial network. Through a masking method, this network can edit the makeup area with emphasis, restrict the area that does not require editing, and maintain the key information. At the same time, it can separately edit the eye shadow, lips, cheeks, and other local areas of the face to achieve makeup on specific areas and enrich the makeup function. In addition, this network can be trained jointly on multiple datasets. In addition to makeup dataset, it can also use other face datasets as an aid to enhance the models generalization ability and get a more natural makeup result. Finally, based on a variety of evaluation methods, more comprehensive qualitative and quantitative experiments are carried out, the results are compared with the other methods, and the performance of the proposed method is comprehensively evaluated.

Abstract:Age is an important attribute of human beings. In recent years, automatic estimations of the user’s agehave been becoming an active topic in pattern recognition, computer vision, voice recognition, human-computerinteraction (HCI), etc. It can be widely used in many real applications such as forensics, e-business, security, and soon. In daily life, people can easily estimate the age of a person according to some visual and audio information (heremainly refers to face and voice) because humans’ faces and voices are important agents of their age. This paperintroduces in detail the models, algorithms used in automatic age estimation based on visual and audio information,as well as their performance and characteristics. The possible future directions for the research in automatic ageestimation are also discussed.

Abstract:Super-Resolution (SR) reconstruction is posed as a Bayesian estimation of the location and appearance parameters of a face model. Image registration and image fusion, the two steps for SR reconstruction, are combined into one unified probabilistic framework, in which the prior information about facial appearance and gray from the face model is incorporated into both of the steps. In addition, a particle filter based algorithm is proposed to achieve the estimation, i.e. SR reconstruction. The proposed approach avoids the inherent dilemma of the most traditional methods, in which it demands a high-resolution image to get an accurate and robust estimation of the motion field, while reconstructing a high-resolution image requires the accurate and robust estimation of motion field. Experiments performed on synthesized frontal face sequences show that the proposed approach gains superior performance both in registration and reconstruction.