58833855
EDFCES: a new example-driven 3D face construction and editing system
This paper presents an automatic runtime system for generating varied, realistic face models by synthesizing a global face shape and local facial features according to intuitive, high-level control parameters. Our method takes as examples 3D face scans in order to exploit the parameter-to-geometry correlations present in real faces. In order to establish the correspondences among the scanned models, we use a three-step model fitting approach to conform a generic head mesh onto each scanned model. We transform the obtained data sets of global face shapes and local feature shapes into vector space representations by applying a principal component analysis (PCA). We compute a set of face anthropometric measurements to parameterize the exemplary shapes in the measurement spaces. Using PCA coefficients as a compact shape representation, we approach the shape synthesis problem by forming scattered data interpolation functions designed to generate the desired face shape by taking anthropometric parameters as input. At runtime, the interpolation functions are evaluated for the input parameter values to produce new face geometries at an interactive rate. The correspondence among all exemplary face textures is obtained by parameterizing the 3D generic mesh over a 2D image domain. The new feature texture with the desired attributes is synthesized by interpolating the example textures. The resulting system is intuitive to control and fine-grained. We demonstrate our method by applying different parameters to generate a wide range of face models.
43457446
Synthesis of facial expressions using a 3D anatomical model
In this paper we propose an individualized 3D facial model constructed from the anatomical perspective for realistic facial expression synthesis. Our model incorporates a physically-based approximation to facial skin tissue, a set of anatomically-motivated facial muscle actuators and underlying skull structure. The skin is modeled as a multi-layer mass-spring lattice by taking into account the nonlinear stress-strain relationship of the real skin. The 3D face model incorporates a skull structure which extends the scope of facial motion and facilitates facial muscle definition. The automatic facial muscle construction is achieved by using an efficient muscle mapping approach that ensures different muscles to be located at the anatomically correct positions between the skin and skull layers. For computational efficiency, we devise a semi-implicit integration method to numerically solve the governing dynamics. The dynamic facial animation algorithm runs at an interactive rate with realistic facial expressions to be synthesized.
39711298
Data-Driven Feature Control Models for Creating Virtual Faces
This paper presents a novel data-driven method for creating realistic face models based on estimated high-level feature control models. Our method takes as examples 3D face scans. By bringing scanned models into full correspondence with a model fitting approach, we apply principal component analysis (PCA) to the exemplar shapes of each facial feature to build a shape space. We compute a set of face anthropometric measurements to parameterize example feature shapes in the measurement spaces. Using the PCA coefficients as a compact shape representation, we approach the shape synthesis problem by forming scattered data interpolation functions that are devoted to the generation of desired shape by taking the anthropometric parameters as input. The correspondence among all exemplar textures is obtained by parameterizing a 3D generic mesh over a 2D image domain. The new feature texture with desired attributes is synthesized by interpolating the example textures.
59703933
Anatomy-based 3D facial modeling for expression animation
In this paper we propose a new hierarchical 3D facial model that conforms to the human facial anatomy for realistic facial expression animation. The facial model has a hierarchical biomechanical structure, incorporating a physically-based approximation to facial skin tissue, a set of anatomically-motivated facial muscle actuators and underlying skull structure. The deformable skin model has multi-layer structure to approximate different types of soft tissue. It takes into account the nonlinear stress-strain relationship of the skin and the fact that soft tissue is almost incompressible. Different kinds of muscle models have been developed to simulate the distribution of the muscle force on the skin due to muscle contraction. By the presence of the skull model, our facial model takes advantage of both more accurate facial deformation and the consideration of facial anatomy during the interactive definition of facial muscles. Under the muscular force, the deformation of the facial skin is evaluated by solving the governing dynamic equation numerically. To improve computational efficiency, we use a localized, semi-implicit integration method which allows a larger time step to be taken in the simulation while retaining stability. The dynamic facial animation algorithm runs at an interactive rate with flexible and realistic facial expressions to be generated.
45254253
A physically-based model for real-time facial expression animation
In this paper we propose a physically-based 3D dynamic facial model based on anatomical knowledge for facial expression animation. The facial model incorporates a physically-based approximation to facial skin and a set of anatomically-motivated facial muscles. The skin model is established through the use of a mass-spring system with nonlinear springs which simulate the elastic dynamics of a real facial skin. Facial muscle models are developed to emulate facial muscle contraction. Lagrangian mechanics governs the dynamics, dictating the deformation of facial surface in response to muscle forces. The dynamic facial modeling algorithm runs at interactive rate with continuous 3D display on a graphics workstation. Using our system, we can effectively synthesize realistic facial expressions and analyze tile relationship between facial surface deformation and the activation of underlying facial muscles.
31519303
Data-Driven Facial Feature Morphing for 3D Face Synthesis
13218093
Notice of Violation of IEEE Publication PrinciplesAchieving flexible task delegation in role-based agent teams
Teamwork has become increasingly important to many disciplines. In our opinion, an effective design of a teamwork language should be able to handle unexpected uncertainties and allow for the conceptual specification of teamwork knowledge for reuse. This paper introduces RoB-MALLET, a teamwork language based on the concepts of role and role variable. An important characteristic of our notion of role is that it includes the specific operations that an agent filling the role must perform in a specific setting as well as their ordering constraints. Taking this view on the definition of role has enabled us to present the responsibility of roles, to reason about the assignment of real agents to roles, and to achieve a certain level of plan reusability. Finally, we present several experiments that explore plan reusability of RoB-MALLET, show that it is flexible in supporting simultaneity of invocation and evaluate its efficiency in supporting teamwork.
5612729
Adaptive Rule Loading and Session Control for Securing Web-Delivered Services
In this paper, we present Arctic, an adaptive reinforcement learning control technique for web intrusion check. A rule-based model is designed to describe the requirement of vulnerability detection. The whole validation rule set is divided into multiple sections,and each can be enabled in either in-line control mode or off-line monitoring mode based on the observation and analysis of user behaviors, balancing security and system cost. For the different sizes of in-line validation rules, we use the reinforcement learning technique to adjust the session admission control, maintaining the response time in an acceptable level as well as maximizing the utilization of system resources. We design a runtime protection mechanism using a HTTP session listener and servlet filters in the J2EE container to intercept HTTP requests and responses. Preliminary results of our implementation are presented in this paper.
8620934
Certifying Concurrent Programs Using Transactional Memory
Transactional memory (TM) is a new promising concurrency-control mechanism that can avoid many of the pitfalls of the traditional lock-based techniques. TM systems handle data races between threads automatically so that programmers do not have to reason about the interaction of threads manually. TM provides a programming model that may make the development of multi-threaded programs easier. Much work has been done to explore the various implementation strategies of TM systems and to achieve better performance, but little has been done on how to formally reason about programs using TM and how to make sure that such reasoning is sound. In this paper, we focus on the semantics of transactional memory and present a proof-carrying code (PCC) system for reasoning about programs using TM. We formalize our reasoning with respect to the TM semantics, prove its soundness, and use examples to demonstrate its effectiveness.
27532025
Modeling and Prediction in the Enzymatic Hydrolysis of Cellulose Using Artificial Neural Networks
Artificial intelligence technique namely artificial neural network (ANN) was used to describe the enzymatic kinetics of cellulose hydrolysis in a heterogeneous system, and compared with response surface methodology (RSM). Three hydrolysis conditions (activity of added cellulase, substrate concentration and time) served as the input of the neural network model, and the glucose content served as the output. The experimental data from Box-Behnken design were used to train the neural network using the back propagation algorithm. The others of 33 design were used to check the performance of the trained network. The ANN modelled and predicted values showed better agreement with the experimentally reported ones than RSM. ANN could mimic the heterogeneous enzymatic hydrolysis of cellulose.
15044843
Learning from facial aging patterns for automatic age estimation
Age Specific Human-Computer Interaction (ASHCI) has vast potential applications in daily life. However, automatic age estimation technique is still underdeveloped. One of the main reasons is that the aging effects on human faces present several unique characteristics which make age estimation a challenging task that requires non-standard classification approaches. According to the speciality of the facial aging effects, this paper proposes the AGES (AGing pattErn Subspace) method for automatic age estimation. The basic idea is to model the aging pattern, which is defined as a sequence of personal aging face images, by learning a representative subspace. The proper aging pattern for an unseen face image is then determined by the projection in the subspace that can best reconstruct the face image, while the position of the face image in that aging pattern will indicate its age. The AGES method has shown encouraging performance in the comparative experiments either as an age estimator or as an age range estimator.