3D/4D facial expression analysis: An advanced annotated face model approach

Fang, Tianhong; Zhao, Xi; Ocegueda, Omar; Shah, Shishir K.; Kakadiaris, Ioannis A.

doi:10.1016/j.imavis.2012.02.004

Cited by 76 publications

(47 citation statements)

References 29 publications

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“…The pairand segment-wise distances between those level curves encoded the spatio-temporal features, and HMM was exploited to model and classify expressions. The work of Fang et al [35], [36] emphasized 4D face data registration and dense corresponding between 3D meshes along the temporal line, and a variant of local binary patters on three orthogonal plane was introduced as both static and dynamic features to find expression labels. Canavan et al [37] proposed to encode the dynamic Shape Index (SI) descriptors (the quantization of principal curvatures) in the temporal axis.…”

Section: B 4d Facial Expression Recognitionmentioning

confidence: 99%

Muscular Movement Model-Based Automatic 3D/4D Facial Expression Recognition

Zhen

Huang

Wang

et al. 2016

IEEE Trans. Multimedia

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Facial expression is an important channel for human nonverbal communication. This paper presents a novel and effective approach to automatic 3D/4D facial expression recognition based on the muscular movement model (MMM). In contrast to most of existing methods, the MMM deals with such an issue in the viewpoint of anatomy. It first automatically segments the input 3D face (frame) by localizing the corresponding points within each muscular region of the reference using iterative closest normal point. A set of features with multiple differential quantities, including coordinate, normal , and shape index values, are then extracted to describe the geometry deformation of each segmented region. Meanwhile, we analyze the importance of these muscular areas, and a score level fusion strategy is exploited to optimize their weights by the genetic algorithm in the learning step. The support vector machine and the hidden Markov model are finally used to predict the expression label in 3D and 4D, respectively. The experiments are conducted on the BU-3DFE and BU-4DFE databases, and the results achieved clearly demonstrate the effectiveness of the proposed method.Index Terms-3D/4D facial expression recognition, muscle movement model (MMM), shape representation.

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Section: B 4d Facial Expression Recognitionmentioning

confidence: 99%

Muscular Movement Model-Based Automatic 3D/4D Facial Expression Recognition

Zhen

Huang

Wang

et al. 2016

IEEE Trans. Multimedia

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“…Specifically, in comparing shapes of faces, it is important that similar biological parts are registered to each other, in particular the left and right halves of the face, when studying the face asymmetry. Several methods have been proposed in the literature as discussed above such as the Non-rigid ICP algorithm (Cheng et al, 2015), the Free Form Deformation (FFD) algorithm (Sandbach et al, 2012) and the Thin-plate Spline (TPS) algorithm (Fang et al, 2012). Most of these solutions try to find an optimal registration between two 3D faces, however, their cost functions which minimize the distance between 3D meshes is not a proper metric; it is not even symmetric.…”

Section: Pre-processing Of 3d Framesmentioning

confidence: 99%

Facial Asymmetry Assessment from 3D Shape Sequences: The Clinical Case of Facial Paralysis

Desrosiers

Bennis

Amor

et al. 2016

Proceedings of the 11th Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications

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Abstract:This paper addresses the problem of quantifying the facial asymmetry from dynamic 3D data. We investigate here the role of 4D (i.e. 3D+time) data to reveal the amount of both static and dynamic asymmetry, in a clinical use-case of facial paralysis. The final goal is to provide tools and solutions to clinicians for facial paralysis assessment and monitoring, which can provide qualitative and quantitative evaluations. To this end, the approach proposed here consider 3D facial sequences and adopt a recently-developed Riemannian approach for facial deformation analysis. After a preprocessing step, each frame of a given 3D sequence is approximated by an indexed collection of elastic radial curves. Riemannian shape analysis of obtained curves and their symmetrical counterparts, both elements of the same shape space, give rise to a feature vector, called Dense Scalar Fields (DSFs). The use of these DSFs reveals the amount of bilateral asymmetry of the face, when conveying expressions. That is, given a 3D frame, it is first reflected with respect to the YZ-plane, then compared to the obtained reflection using the DSFs. To exemplify the use of the proposed approach, a new dataset have been collected (of patients) before and after injecting Botulinum Toxin (BT) in related facial muscles. Experimental results obtained on this dataset show that the proposed approach allows clinicians to evaluate the facial asymmetry before and after the medical treatment.

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“…The model was particular effective for capturing faces for the purposes of face recognition and verification, due to the fact that fitting relayed of deforming a single facial template, in many cases, it was unable to capture facial deformations due to facial expressions [17]. In [17] in order to better capture facial expressions an extra fitting strategy using Thin Plate Splines (TPS) was applied.…”

Section: Related Workmentioning

confidence: 99%

Active nonrigid ICP algorithm

Cheng

Marras

Zafeiriou

et al. 2015

2015 11th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition (FG)

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Abstract-The problem of fitting a 3D facial model to a 3D mesh has received a lot of attention the past 15-20 years. The majority of the techniques fit a general model consisting of a simple parameterisable surface or a mean 3D facial shape. The drawback of this approach is that is rather difficult to describe the non-rigid aspect of the face using just a single facial model. One way to capture the 3D facial deformations is by means of a statistical 3D model of the face or its parts. This is particularly evident when we want to capture the deformations of the mouth region. Even though statistical models of face are generally applied for modelling facial intensity, there are few approaches that fit a statistical model of 3D faces. In this paper, in order to capture and describe the non-rigid nature of facial surfaces we build a part-based statistical model of the 3D facial surface and we combine it with non-rigid iterative closest point algorithms. We show that the proposed algorithm largely outperforms state-of-the-art algorithms for 3D face fitting and alignment especially when it comes to the description of the mouth region.

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3D/4D facial expression analysis: An advanced annotated face model approach

Cited by 76 publications

References 29 publications

Muscular Movement Model-Based Automatic 3D/4D Facial Expression Recognition

Muscular Movement Model-Based Automatic 3D/4D Facial Expression Recognition

Facial Asymmetry Assessment from 3D Shape Sequences: The Clinical Case of Facial Paralysis

Active nonrigid ICP algorithm

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