3D Human Face Reconstruction Research Articles

Improved 3D human face reconstruction from 2D images using blended hard edges

This study reports an effective and robust edge-based scheme for the reconstruction of 3D human faces from input of single images, addressing drawbacks of existing methods in case of large face pose angles or noisy input images. Accurate 3D face reconstruction from 2D images is important, as it can enable a wide range of applications, such as face recognition, animations, games and AR/VR systems. Edge features extracted from 2D images contain wealthy and robust 3D geometric information, which were used together with landmarks for face reconstruction purpose. However, the accurate reconstruction of 3D faces from contour features is a challenging task, since traditional edge or contour detection algorithms introduce a great deal of noise, which would adversely affect the reconstruction. This paper reports on the use of a hard-blended face contour feature from a neural network and a Canny edge extractor for face reconstruction. The quantitative results indicate that our method achieves a notable improvement in face reconstruction with a Euclidean distance error of 1.64 mm and a normal vector distance error of 1.27 mm when compared to the ground truth, outperforming both traditional and other deep learning-based methods. These metrics show particularly significant advancements, especially in face shape reconstruction under large pose angles. The method also achieved higher accuracy and robustness on in-the-wild images under conditions of blurring, makeup, occlusion and poor illumination.

Evaluating 3D human face reconstruction from a frontal 2D image, focusing on facial regions associated with foetal alcohol syndrome

Foetal alcohol syndrome (FAS) is a preventable condition caused by maternal alcohol consumption during pregnancy. The FAS facial phenotype is an important factor for diagnosis, alongside central nervous system impairments and growth abnormalities. Current methods for analysing the FAS facial phenotype rely on 3D facial image data, obtained from costly and complex surface scanning devices. An alternative is to use 2D images, which are easy to acquire with a digital camera or smart phone. However, 2D images lack the geometric accuracy required for accurate facial shape analysis. Our research offers a solution through the reconstruction of 3D human faces from single or multiple 2D images. We have developed a framework for evaluating 3D human face reconstruction from a single-input 2D image using a 3D face model for potential use in FAS assessment. We first built a generative morphable model of the face from a database of registered 3D face scans with diverse skin tones. Then we applied this model to reconstruct 3D face surfaces from single frontal images using a model-driven sampling algorithm. The accuracy of the predicted 3D face shapes was evaluated in terms of surface reconstruction error and the accuracy of FAS-relevant landmark locations and distances. Results show an average root mean square error of 2.62 mm. Our framework has the potential to estimate 3D landmark positions for parts of the face associated with the FAS facial phenotype. Future work aims to improve on the accuracy and adapt the approach for use in clinical settings. Significance: Our study presents a framework for constructing and evaluating a 3D face model from 2D face scans and evaluating the accuracy of 3D face shape predictions from single images. The results indicate low generalisation error and comparability to other studies. The reconstructions also provide insight into specific regions of the face relevant to FAS diagnosis. The proposed approach presents a potential cost-effective and easily accessible imaging tool for FAS screening, yet its clinical application needs further research.

3D reconstruction of human face from an input image under random lighting condition

The three-dimensional reconstruction from single input image is quite difficult due to many unknown parameters, such as the light conditions, the surface normal and albedo of the object. However, there are overall similar characteristics for different human faces, such as the shapes and the positions of the eyes, nose; mouth and ears are generally identical. The similar characteristics has been used in this paper to relax the numbers of the input face images, and reconstruct the 3D shape based on a couple statistical model. Moreover, the light condition of the single input image can be different from that of training database. The experiment results show the effectiveness of the proposed method.

3D reconstruction of human face from an input image under random lighting condition

3D reconstruction of human face from an input image under random lighting condition

3D human face reconstruction based on band-limited binary patterns

Face recognition technology has great prospects for practical applications. Three-dimensional (3D) human faces are becoming more and more important in consideration of the limits of two-dimensional face recognition. We propose an active binocular setup to obtain a 3D colorful human face using the band-limited binary patterns (BBLP) method. Two grayscale cameras capture the BBLP projected onto the target of human face by a digital light processing (DLP) projector synchronously. Then, a color camera captures a colorful image of the human face. The benefit of this system is that the 3D colorful human face can be obtained easily with an improved temporal correlation algorithm and the precalibration results between three cameras. The experimental results demonstrated the robustness, easy operation, and the high speed of this 3D imaging setup.

RECONSTRUCT 3D HUMAN FACE USING TWO ORTHOGONAL IMAGES

As the 3D human face reconstruction is becoming very popular in recent times, it attracts many researchers. Construction of 3D human face using only two orthogonal images and twelve landmark features are the main context of the proposed approach. For3D object modeling, the Open Graphics Library (OpenGL) is used as the platform through which modeling, modification and rendering is performed on the morphable model. The proposed approach trails through semi-automatic identification of the facial landmark features, calculation of the 3D coordinates of human face, morphable model construction in OpenGL, reshaping of the morphable model and rendering of the morphable model. The facial landmark identification is semiautomatic method, as the module requires the manual interaction for marking the facial landmarks on the image. The reshaping of morphable model is required as the morphable model does not fit to the actual face in most of the cases. The morphable model is reshaped by calculating root mean square (RMS) error of face coordinates. The rendering process does not require a wide screen image because the approach performs rendering using input front face image and side face image as textures. Applications of this research help to overcome the disputes of fields like crime detection, 3D game characterization, the ornaments exhibition and in the area of medical technology like plastic surgery.

3D reconstruction of human face based on an improved seeds-growing algorithm

An algorithm based on binocular stereo vision is proposed to generate 3D (three-dimensional) dense points cloud model of the human face. A two-step matching strategy from sparse to dense is developed. Firstly, an improved seeds-growing algorithm is utilized to acquire sparse matching of high confidence. Secondly, based on the control points method and piecewise dynamic programming, the dense matching is completed. Experimental results show that the proposed algorithm can produce smooth and dense 3D points cloud model of the human face.