Merging Face Research Articles

Robust surface segmentation and edge feature lines extraction from fractured fragments of relics

Abstract Surface segmentation and edge feature lines extraction from fractured fragments of relics are essential steps for computer assisted restoration of fragmented relics. As these fragments were heavily eroded, it is a challenging work to segment surface and extract edge feature lines. This paper presents a novel method to segment surface and extract edge feature lines from triangular meshes of irregular fractured fragments. Firstly, a rough surface segmentation is accomplished by using a clustering algorithm based on the vertex normal vector. Secondly, in order to differentiate between original and fracture faces, a novel integral invariant is introduced to compute the surface roughness. Thirdly, an accurate surface segmentation is implemented by merging faces based on face normal vector and roughness. Finally, edge feature lines are extracted based on the surface segmentation. Some experiments are made and analyzed, and the results show that our method can achieve surface segmentation and edge extraction effectively.

Investigation of turbulent channel flow using local mesh refinement

Large eddy simulation of turbulent channel flow is carried out at Reτ values of 150 and 395. Mesh refinement effects are evaluated on conventional meshes, then local mesh refinement in streamwise direction is applied and investigated. The goal of the paper is to give proposal about the ideal position of the merging face and to show how many cells can be saved in this case without losing too much accuracy at both Reynolds numbers. The results are compared to DNS data.

Method of merging face detection windows based on Euclidean distance

To address the problem of the coincidence of the position and size for a same face cannot be guaranteed in different results due to the scale transformation in face detection,research has been done by the method of merging windows in face-detection employing statistical training.And a method based on Euclidean distance was proposed too,without considering the case of error-detected and undetected faces.Judging circle and Euclidean distance were employed to merge face-detection windows according to the distribution of center coordinates.Verifying experiments of the method were conducted according to different pictures,and the experimental results proved the method simple and effective.

CAD model simplification using a removing details and merging faces technique for a FEM simulation

The simulation process is currently used in the design and dimensioning of mechanical parts. With the progress of computer materials, the finite elements method (FEM) becomes the most used approach in the simulation of mechanical behaviour. The simulation process needs multiple Design-FEM loops. In order to accelerate those analysis loops, an adaptation of computer aided design (CAD) model is necessary. The adaptation step consists in the simplification of the CAD model geometry by eliminating details (holes, chamfers, fillet, etc.) and faces. In this paper, a novel technique of simplification of the CAD geometry is developed. This technique is a hybrid method based on a combination of the elimination details and merging faces. The merging of faces is based on the energetic method. With this approach, the computing time is reduced by the elimination of geometric details which do not boundary conditions. An implementation of the proposed algorithm on the Open Cascade platform is also presented. The results of the developed method are compared with a previous publication. The reduction of the computing time and the amelioration of the result precision highlight the efficiency of the presented method.

M-Face: An Appearance-Based Photorealistic Model for Multiple Facial Attributes Rendering

A novel framework for appearance-based photorealistic facial modeling, called Merging Face (M-Face), is presented and applied to generate emotional facial attributes in rotated views. Assuming that human faces belong to both the linear object class and the Lambertian object class, we span the face space and attribute space, respectively, by using groups of prototypes and merging ratio image (MRI). The MRI is defined as the seamless blend of individual expression ratio image, aging ratio image, and illumination ratio image (quotient image). The M-Face integrates the view space projection, shape caricaturing, and texture MRI-mapping techniques. Derived from the average face, the caricatured shape is reshaped to be more distinct by exaggerating individual distinctiveness, while the rerendered texture multiplies the MRI information during the caricaturing. Based on the M-Face model, the expression morphing, chronological aging or rejuvenating, and illumination variance can be merged seamlessly in a photorealistic style on desired view-rotated faces yielded by view morphing. This framework has the following advantages. First, 3-D reconstruction is avoided without weakening photorealistic effects. Second, the integration of shape caricaturing and texture MRI-mapping proves by experiments to be an efficient and computational inexpensive strategy for realistic face synthesis. Finally, the M-Face is a 2-D parameter-driven model, which highly simplifies user manipulations. The potential applications of M-Face are in various fields like virtual human face, speech-driven talking head, digital painting, film-making, and low-bit-rate communication

Merging face and finger images for human identification

Human identification performance reported so far using face or finger images under certain conditions is good practice, however, there is still a great need for better performance in biometrics for use in video surveillance. One possible way to achieve improved performance is to combine information from multiple sources. Besides, such systems alleviate some of the problems that are faced by single biometrics-based systems like restricted degrees of freedom, spoof attacks, and unacceptable error rates. We present a prototype bimodal biometric identification system by merging face and finger images. A novel approach is adopted to merge biometric (face and finger) traits of an individual to one image (containing features of both), named merged pattern. The integrated features are then extracted with an adaptive artificial neural network. The proposed algorithm is shown to exhibit robustness in achieving better classification results with both good generalization performance and a fast training/test time using variable public domain databases. Sources of variability include facial expression, gender, individual appearance, tilt, lighting conditions, and occluding objects (hair, spectacles, etc).

The employment law implications of charity mergers

Examines the employment law implications for charities considering merger. Considers the employment law problems that are involved in charity mergers and the different strategies that have been employed in practice to deal with them. Charities merging face particular problems, not only from the natural concerns of staff about their jobs post‐merger, but also because the law which protects employees when organisations merge is not clear. The application of the relevant legal principles may be particularly uncertain in the context of mergers between charities due to the variety in the legal forms of the merger, and in the employment practices adopted by charities. Illustrates these concerns with an empirical study of a number of charity mergers.