A three-dimensional muscle-based facial expression synthesizer for model-based image coding

Lei, Yuong-Wei; Wu, Ja‐Ling; Ouhyoung, Ming

doi:10.1016/0923-5965(95)00057-7

Cited by 6 publications

(4 citation statements)

References 12 publications

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“…The need for searching 2D points in the images is a serious disadvantage because of the dependence on the acquisition conditions. Some applications of the use of a generic 3D head model are: analysis and synthesis of facial expressions (Lei et al, 1996), analysis of robustness to illumination or pose correction in an AFR systems (Lee and Ranganath, 2003), generation of synthetic faces to augment the number of variations of each individual of the training set (Vetter, 1999), among others.…”

Section: D Face Models To Extract Features From 2d Datamentioning

confidence: 99%

Three-dimensional facial surface modeling applied to recognition

Moreno¹,

Sánchez²,

Frı́as-Martı́nez³

et al. 2009

Engineering Applications of Artificial Intelligence

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Applications related to game technology, law-enforcement, security, medicine or biometrics are becoming increasingly important, which, combined with the proliferation of three-dimensional (3D) scanning hardware, have made that 3D face recognition is now becoming a promising and feasible alternative to 2D face methods. The main advantage of 3D data, when compared with traditional 2D approaches, is that it provides information that is invariant to rigid geometric transformations and to pose and illumination conditions. One key element for any 3D face recognition system is the modeling of the available scanned data. This paper presents new 3D models for facial surface representation and evaluates them using two matching approaches: one based on Support Vector Machines and another one on Principal Component Analysis (with a Euclidean classifier). Also, two types of environments were tested in order to check the robustness of the proposed models: a controlled environment with respect to facial conditions (i.e. expressions, face rotations, etc) and a noncontrolled one (presenting face rotations and pronounced facial expressions). The recognition rates obtained using reduced spatial resolution representations (a 77.86 % for non-controlled environments and a 90.16% for controlled environments, respectively) show that the proposed models can be effectively used for practical face recognition applications.

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Section: D Face Models To Extract Features From 2d Datamentioning

confidence: 99%

Three-dimensional facial surface modeling applied to recognition

Moreno¹,

Sánchez²,

Frı́as-Martı́nez³

et al. 2009

Engineering Applications of Artificial Intelligence

View full text Add to dashboard Cite

show abstract

“…One common holistic technique on 3D object recognition is based on the correspondence among scene points and model points in order to determinate the object pose and location and to extract features to perform the recognition 35 . The use of a generic 3D head model 36 has some applications like analysis and synthesis of facial expressions 41 , illumination or pose correction in a face recognition system 39 , descriptor extraction 40 and generation of synthetic face variations (i.e. illumination changes, rotations...) to augment the number of training views of each individual 62,34 (to increase robustness when there exist such variations).…”

Section: Holistic 3d Face Modellingmentioning

confidence: 99%

Robust Representation of 3d Faces for Recognition

Moreno

Sánchez

Frı́as-Martı́nez

2006

Int. J. Patt. Recogn. Artif. Intell.

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Automatic face recognition is becoming increasingly important due to the security applications derived from it. Although the face recognition problem has focused on 2D images, recently, due to the proliferation of 3D scanning hardware, 3D face recognition has become a feasible application. This 3D approach do not use the colour of the faces, so, when it is compared to the more traditional 2D approach, has the main advantages of being robust under lighting variations and also of providing more relevant information. In this paper we present a new 3D face model based on curvature properties of the face surface. As will be presented, our system is able to detect the subset of characteristics of the face with higher discrimination power among a large set of them. The robustness of the model is tested by comparing recognition rates using both controlled and non-controlled environments regarding facial expressions and face rotations. The difference between the recognition rate of the two environments of only 5% proves that the model has a high degree of robustness against pose and facial expressions and that is robust enough to implement facial recognition applications, which can achieve up to 91% correct recognition rate.

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“…-La animación por computador [Lei96], empleada en las industrias dedicadas al cine, vídeos o videojuegos, también demandan una animación realista de las caras humanas.…”

Section: Análisis Y Síntesis De Expresiones Facialesunclassified

“…•49-En [Lei96] se presenta un método de síntesis de expresiones faciales 3D basado en músculos. La síntesis se efectúa mediante contracciones de 19 músculos para modificar un modelo 3D de la cabeza humana incluyendo la rotación de la mandíbula, utilizando 22 puntos faciales.…”

Section: Análisis Y Síntesis De Expresiones Facialesunclassified

Reconocimiento facial automático mediante técnicas de visión tridimensional

Díaz¹

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This Ph. D. Thesis presents new approaches to Automatic Face Recognition using 3D Vision techniques. 3D Vision is the part of Computer Vision that uses three-dimensional properties of a real scene (obtained from its digitizatlon) to achieve its interpretation. This Tfiesis focuses on tlie recognition of human faces using 3D information.New representations of 3D shapes are proposed in this Thesis in order to implement systems that are based on 3D holistic information of the face. Principal Components Analysis (PCA) and Support Vector Machines (SVM), two widely used techniques for 2D images, have been successfiílly used in combination with these new 3D representations to implement Face Recognition Systems.In order to efficiently use PCA and SVM tlie Thesis describes the design of a stage that corrects the position of 3D images and that obtains a volumetric regular representation using voxels. This new representation, obtained from facial meshes, is an alternative to the depth maps that have been employed until now in this kind of holistic 3D face recognition systems.

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A three-dimensional muscle-based facial expression synthesizer for model-based image coding

Cited by 6 publications

References 12 publications

Three-dimensional facial surface modeling applied to recognition

Three-dimensional facial surface modeling applied to recognition

Robust Representation of 3d Faces for Recognition

Reconocimiento facial automático mediante técnicas de visión tridimensional

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