Passive Three Dimensional Face Recognition Using Iso-Geodesic Contours and Procrustes Analysis

Jahanbin, Sina; Jahanbin, R.; Bovik, Alan C.

doi:10.1007/s11263-013-0631-2

Cited by 17 publications

(13 citation statements)

References 38 publications

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“…As is pointed out in literature [15], the accuracy of AAM algorithm to locate the landmarks is only 70 % under the circumstance that error satisfies m e 0:1. However, the accuracy of the algorithm locating landmarks in 2D faces in both literature [13] and this paper is over 99 % under the same circumstance. But compared with the automatic landmark localization algorithm pro-posed in this paper, the algorithm in literature [13] is only semi-automatic because its coarse landmark localization area cannot be completely determined automatically.…”

Section: Landmark Localization Results Comparingmentioning

confidence: 63%

“…However, the accuracy of the algorithm locating landmarks in 2D faces in both literature [13] and this paper is over 99 % under the same circumstance. But compared with the automatic landmark localization algorithm pro-posed in this paper, the algorithm in literature [13] is only semi-automatic because its coarse landmark localization area cannot be completely determined automatically. Figure 7 describes the relationship between permissible error m e 0:1 and the accuracy of 2D landmark localization resulting from the algorithm in literature [13] and this paper.…”

Section: Landmark Localization Results Comparingmentioning

confidence: 63%

“…But compared with the automatic landmark localization algorithm pro-posed in this paper, the algorithm in literature [13] is only semi-automatic because its coarse landmark localization area cannot be completely determined automatically. Figure 7 describes the relationship between permissible error m e 0:1 and the accuracy of 2D landmark localization resulting from the algorithm in literature [13] and this paper. We can also conclude that the algorithm in this paper has a better performance than that of the algorithm in literature [13] from it.…”

Section: Landmark Localization Results Comparingmentioning

confidence: 99%

“…Figure 7 describes the relationship between permissible error m e 0:1 and the accuracy of 2D landmark localization resulting from the algorithm in literature [13] and this paper. We can also conclude that the algorithm in this paper has a better performance than that of the algorithm in literature [13] from it. Table 3 displays the results of the comparison among three 3D landmark detection algorithms.…”

Section: Landmark Localization Results Comparingmentioning

confidence: 99%

“…In [13], a set of 50 facial images with different races, ages and genders was selected as the training and Gabor bunch extraction to detect landmarks. This algorithm achieves a good result on portrait image, but performs relatively poor on range image.…”

Section: "Gabormentioning

confidence: 99%

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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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Section: Landmark Localization Results Comparingmentioning

confidence: 63%