Yuankui Hu

Bio: Yuankui Hu is an academic researcher from University of Science and Technology of China. The author has contributed to research in topics: Eigenface & 3D single-object recognition. The author has an hindex of 1, co-authored 1 publications receiving 15 citations.

Reconstruction of 3D Face from a Single 2D Image for Face Recognition

Abstract: In this paper, a synthetic exemplar based framework for face recognition with variant pose and illumination is proposed. Our purpose is to construct a face recognition system only according to one single frontal face image of each person for recognition. The framework consists of three main parts. First, a deformation based 3D face modeling technique is introduced to create an individual 3D face model from a single frontal face image of a person with a generic 3D face model. Then, the virtual faces for recognition at various lightings and views are synthesized. Finally, an Eigenfaces based classifier is constructed where the virtual faces synthesized are used as training exemplars. The experimental results show that the proposed 3D face modeling technique is efficient and the synthetic face exemplars can significantly improve the accuracy of face recognition with variant pose and illumination.

A Video, Text, and Speech-Driven Realistic 3-D Virtual Head for Human–Machine Interface

Abstract: A multiple inputs-driven realistic facial animation system based on 3-D virtual head for human–machine interface is proposed. The system can be driven independently by video, text, and speech, thus can interact with humans through diverse interfaces. The combination of parameterized model and muscular model is used to obtain a tradeoff between computational efficiency and high realism of 3-D facial animation. The online appearance model is used to track 3-D facial motion from video in the framework of particle filtering, and multiple measurements, i.e., pixel color value of input image and Gabor wavelet coefficient of illumination ratio image, are infused to reduce the influence of lighting and person dependence for the construction of online appearance model. The tri-phone model is used to reduce the computational consumption of visual co-articulation in speech synchronized viseme synthesis without sacrificing any performance. The objective and subjective experiments show that the system is suitable for human–machine interaction.

Data-driven 3D visual pronunciation of Chinese IPA for language learning

Abstract: In the framework of intelligent aided language learning, a real-time data-driven 3D visual pronunciation system of Chinese IPA is proposed. First, a high quality articulatory speech corpus including speech and 3D articulatory data of lips, tongue and jaw movements is collected through Electro-Magnetic Articulograph; second, the 3D articulatory modeling including shape design and motion synthesis is conducted. The articulatory shape is obtained by designing a precise 3D facial model including internal and external articulators. The articulatory motion synthesis is obtained combining parameterized model and anatomical model. The system can thus illustrate the slight differences among phonemes by synthesizing both internal and external articulatory movements. The perceptual evaluation shows the suitability of the system for instructing language learners to articulate.

A Video-Based Facial Motion Tracking and Expression Recognition System

Abstract: We proposed a facial motion tracking and expression recognition system based on video data. By a 3D deformable facial model, the online statistical model (OSM) and cylinder head model (CHM) were combined to track 3D facial motion in the framework of particle filtering. For facial expression recognition, a fast and efficient algorithm and a robust and precise algorithm were developed. With the first, facial animation and facial expression were retrieved sequentially. After that facial animation was obtained, facial expression was recognized by static facial expression knowledge learned from anatomical analysis. With the second, facial animation and facial expression were simultaneously retrieved to increase the reliability and robustness with noisy input data. Facial expression was recognized by fusing static and dynamic facial expression knowledge, the latter of which was learned by training a multi-class expressional Markov process using a video database. The experiments showed that facial motion tracking by OSM+CHM is more pose robust than that by OSM, and the facial expression score of the robust and precise algorithm is higher than those of other state-of-the-art facial expression recognition methods.

View invariant head recognition by Hybrid PCA based reconstruction

Abstract: We propose a novel method for 3D head reconstruction and view-invariant recognition from single 2D images. We employ a deterministic Shape From Shading (SFS) method with initial conditions estimated by Hybrid Principal Component Analysis (HPCA) and multi-level global optimization with error-dependent smoothness and integrability constraints. Our HPCA algorithm provides initial estimates of 3D range mapping for the SFS optimization, which is quite accurate and yields much improved 3D head reconstruction. The paper also includes significant contributions in novel approaches to global optimization and in SFS handling of variable and unknown surface albedo, a problem with unsatisfactory solutions by prevalent SFS methods. In the experiments, we reconstruct 3D head range images from 2D single images in different views. The 3D reconstructions are then used to recognize stored model persons. Empirical results show that our HPCA based SFS method provides 3D head reconstructions that notably improve the accuracy compared to other approaches. 3D reconstructions derived from side view (profile) images of 40 persons are tested against 80 3D head models and a recognition rate of over 90% is achieved. Such a capability was not demonstrated by any other method we are aware of.

Real-Time 3-D Facial Animation: From Appearance to Internal Articulators

Abstract: A real-time 3-D facial animation system produces animation for appearance and internal articulators. For appearance, an anatomical model, including the skeleton, muscle, and skin, is built based on anatomical characteristics and a data-driven model is obtained by learning the mapping between texture and depth. Then, the two models are combined to produce animations with various strengths, since the anatomical model can control the animation strength directly and the data-driven model can capture the nuances of facial motion. For internal articulators, tongue tissue arrangements are obtained from medical data. Then, a nonlinear, quasi-incompressible, isotropic, hyperelastic biomechanical model is applied to describe tongue tissues and an anisotropic biomechanical model is applied to reflect the active and passive mechanical behavior of tongue muscle fibers. The tongue animation is simulated using the finite-element method for realism, while the collisions between the tongue and other articulators are simulated with a mass-spring model for efficiency. Experiments show that the system achieves high perceptual evaluation scores and quantitative improvements are demonstrated in the objective evaluation and user studies, compared with the outputs of other systems.