A 3D Statistical Facial Feature Model and Its Application on Locating Facial Landmarks

Zhao, Xi; Dellandréa, Emmanuel; Chen, Liming

doi:10.1007/978-3-642-04697-1_64

Cited by 13 publications

(7 citation statements)

References 18 publications

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“…In [34], 20-mm precision is used for successfully located landmarks. In [35], it is indicated that 99.09% of samples are located within 10-mm precision. Table III also shows (the bottom row) the accuracy of the proposed method under the error measure .…”

Section: Accuracy Of Landmarkingmentioning

confidence: 99%

A Statistical Method for 2-D Facial Landmarking

Dibeklioğlu

Salah

Gevers

2012

IEEE Trans. on Image Process.

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Abstract-Many facial-analysis approaches rely on robust and accurate automatic facial landmarking to correctly function. In this paper, we describe a statistical method for automatic facial-landmark localization. Our landmarking relies on a parsimonious mixture model of Gabor wavelet features, computed in coarse-to-fine fashion and complemented with a shape prior. We assess the accuracy and the robustness of the proposed approach in extensive cross-database conditions conducted on four face data sets (Face Recognition Grand Challenge, Cohn-Kanade, Bosphorus, and BioID). Our method has 99.33% accuracy on the Bosphorus database and 97.62% accuracy on the BioID database on the average, which improves the state of the art. We show that the method is not significantly affected by low-resolution images, small rotations, facial expressions, and natural occlusions such as beard and mustache. We further test the goodness of the landmarks in a facial expression recognition application and report landmarking-induced improvement over baseline on two separate databases for video-based expression recognition (Cohn-Kanade and BU-4DFE).

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Section: Accuracy Of Landmarkingmentioning

confidence: 99%

A Statistical Method for 2-D Facial Landmarking

Dibeklioğlu

Salah

Gevers

2012

IEEE Trans. on Image Process.

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“…Incidentally, models provided in the testing database, BU-3DFE, are already rather well aligned, so we did not find appropriate to describe such a method in this paper. Numerous approaches have been proposed for this problem in 3D FER, as in [7] or [13].…”

Section: Overview Of the Proposed Approachmentioning

confidence: 99%

“…Feature-based approaches are [8], [9], [10], [11], [12], [13] and [14]. They mostly require accurate localization of fiducial points, and analyze their configuration as well as the topology of their neighborhood for 3D FER.…”

Section: Introductionmentioning

confidence: 99%

“…Those distances were then fed into a standard classifier like multiboosting or Support Vector Machines (SVM) to handle the classification. [13] proposed to use the SFAM, a statistical learning system for automatically determining the position of feature points. They later classified the expression using the position of those feature points through a Bayesian Belief Network.…”

Section: Introductionmentioning

confidence: 99%

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Fully automatic 3D facial expression recognition using a region-based approach

Lemaire

Amor

Ardabilian

et al. 2011

Proceedings of the 2011 Joint ACM Workshop on Human Gesture and Behavior Understanding

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Abstract-In this paper, we propose an holistic, fully automatic approach to 3D Facial Expression Recognition (FER). A novel facial representation, namely Differential Mean Curvature Maps (DMCMs), is proposed to capture both global and local facial surface deformations which typically occur during facial expressions. These DMCMs are directly extracted from 3D depth images, by calculating the mean curvatures thanks to an integral computation. To account for facial morphology variations, they are further normalized through an aspect ratio deformation. Finally, Histograms of Oriented Gradients (HOG) are applied to regions of these normalized DMCMs and allow for the generation of facial features that can be fed to the widely used Multiclass-SVM classification algorithm. Using the protocol proposed by Gong et al.[1] on the BU-3DFE dataset, the proposed approach displays competitive performance while staying entirely automatic.

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“…Zhao et al [6] proposed a statistic-based algorithm, which detects landmarks by combining a global training deformation model, a local texture model of landmark, and local shape descriptor. Experiments on FRGC v1.0 achieve a high successful rate of 99.09 %.…”

Section: Introductionmentioning

confidence: 99%

An Automatic Landmark Localization Method for 2D and 3D Face

Liu

Deng

et al. 2015

Lecture Notes in Computer Science

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Abstract. We propose an automatically and accurately facial landmark localization algorithm based on Active Shape Model (ASM) and Gabor Wavelets Transformation (GWT), which can be applied to both 2D and 3D facial data. First, ASM is implemented to acquire landmarks' coarse areas. Then similarity maps are obtained by calculating the similarity between sets of Gabor jets at initial coarse positions and sets of Gabor bunches modeled by its corresponding manually marked landmarks in the training set. The point with the maximum value in each similarity map is extracted as its final facial landmark location. It is showed in our laboratory databases and FRGC v2.0 that the algorithm could achieve accurate localization of facial landmarks with state-of-the-art accuracy and robustness.

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A 3D Statistical Facial Feature Model and Its Application on Locating Facial Landmarks

Cited by 13 publications

References 18 publications

A Statistical Method for 2-D Facial Landmarking

A Statistical Method for 2-D Facial Landmarking

Fully automatic 3D facial expression recognition using a region-based approach

An Automatic Landmark Localization Method for 2D and 3D Face

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