![]() To effectively map low-quality 2D images and 3D depth maps to realistic facial expressions, we introduce a novel face tracking algorithm that combines geometry and texture registration with pre-recorded animation priors in a single optimization. The simplicity of this acquisition device comes at the cost of high noise levels in the acquired data. The user is recorded in a natural environment using a non-intrusive, commercially available 3D sensor. This paper presents a system for performance-based character animation that enables any user to control the facial expressions of a digital avatar in realtime. Implemented on a conventional desktop, face detection proceeds at 15 frames per second. The system yields face detection performance comparable to the best previous systems (Sung and Poggio, 1998 Rowley et al., 1998 Schneiderman and Kanade, 2000 Roth et al., 2000). A set of experiments in the domain of face detection is presented. The third contribution is a method for combining classifiers in a cascade which allows background regions of the image to be quickly discarded while spending more computation on promising face-like regions. The second is a simple and efficient classifier which is built using the AdaBoost learning algorithm (Freund and Schapire, 1995) to select a small number of critical visual features from a very large set of potential features. The first is the introduction of a new image representation called the Integral Image which allows the features used by our detector to be computed very quickly. This paper describes a face detection framework that is capable of processing images extremely rapidly while achieving high detection rates. Our results demonstrate that, by fitting a facial model to the user's face, the rendered virtual 3D objects look more realistic. We validate the performance of our system on eight different subjects (four male and four female) and show results numerically and visually. The last step projects and renders the 3D object into the original image, with enhanced precision and in proper scale, showing the selected object in the user's face. At the end, we deform the chosen 3D objects from its rest shape to a deformed shape matching the specific facial shape of the user. These landmarks and the point cloud reconstructed from the depth information are combined to optimize a 3D facial morphable model that fits as good as possible to the user's head and face. By capturing the image and depth information of a user through a low-cost RGB-D camera, we apply a face tracking technique to detect specific landmarks in the facial image. This paper presents a virtual try-on system to correctly visualize 3D objects (e.g., glasses) in the face of a given user. The results indicated that by using this system, users can find a suitable makeup style for themselves and get help increasing their makeup creativity. A subjective evaluation was conducted to evaluate the users’ satisfaction. Moreover, by using a computer-based drawing system, users can undo, save, and load their makeup image and compare with other styles. ![]() Users can perceive a realistic makeup feeling from our touch detectable makeup tools. The results will be mapped to the user's face in real time by using a projection mapping technique that allows users to see which color is suitable for their skin tone. ![]() While users apply makeup, the program will generate color on the model, which synchronizes with the user's movement. A face painting application was developed based on the Unity 3D framework. The makeup tools were developed for providing a tangible interaction. In this system, facial feature points were tracked by Kinect and mapped to the 3D face model. This study presents a novel interactive face makeup system that aims to support users to improve makeup creativity.
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