Passive Multimodal 2-D+3-D Face Recognition Using Gabor Features and Landmark Distances

Jahanbin, Sina; Choi, Hyun-Jun; Bovik, Alan C.

doi:10.1109/tifs.2011.2162585

Cited by 35 publications

(20 citation statements)

References 49 publications

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“…The validation partition for assessing performance of an approach includes 4007 scans belonging to 466 individuals (Fall 2003 andSpring 2004). Since there was a significant time-lapse between the optical camera and the operation of the laser range finder in the data acquisition, 2D and 3D images are usually out of correspondence [9].…”

Section: Experimental Results and Evaluationmentioning

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: Experimental Results and Evaluationmentioning

confidence: 99%

“…However, the algorithm relies heavily on local descriptors. Jahanbin et al [9] proposed a 2D and 3D multimodal algorithm based on Gabor features, which achieves high accuracy.…”

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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“…Different landmark detection methods in depth or 3D data can be found in the literature, such as [5], [18], [27] and [36]. Numerous approaches have been used to tackle this problem, for example the work described in [5] proposed a heuristic method combined with a SIFT detector applied to local search windows to locate 9 landmarks automatically.…”

Section: D Face Labellingmentioning

confidence: 99%

“…Numerous approaches have been used to tackle this problem, for example the work described in [5] proposed a heuristic method combined with a SIFT detector applied to local search windows to locate 9 landmarks automatically. In the approach presented in [27], 11 fiducial points are automatically located on a pair of range and portrait images using a search over an area centred at the average location of the fiducial point location in the training data. The technique explained in [36] selects 3 feature points by determining the local shape index at each point within the 2.5D scan.…”

Section: D Face Labellingmentioning

confidence: 99%

Persistent homology to analyse 3D faces and assess body weight gain

et al. 2016

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In this paper we analyse patterns in face shape variation due to weight gain. We propose the use of persistent homology descriptors to get geometric and topological information about the configuration of anthropometric 3D face landmarks. In this way, evaluating face changes boils down to comparing the descriptors computed on 3D face scans taken at different times. By applying dimensionality reduction techniques to the dissimilarity matrix of descriptors, we get a space in which each face is a point and face shape variations are encoded as trajectories in that space. Our results show that persistent homology is able to identify features which are well-related to overweight, and may help assessing individual weight trends. The research was carried out in the context of the European project SEMEOTICONS, which developed a multisensory platform which detects and monitors over time facial signs of cardio-metabolic risk.

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“…The Gabor filters, which could effectively extract the image local directional features on multiple scales, have been successfully and prevalently used in FR [16][17] [18]. Very recently, Zhou et al [47] proposed to combine the perceptual features by Gabor filtering with diffusion distance for FR; Du et al [48] proposed to perform FR with non-uniform multilevel selection of Gabor features instead of the uniform down-sampling of Gabor features; a local Gabor based FR with improved accuracy by the selection of Gabor jets was presented in [49]; and multimodal FR using Gabor feature was presented in [50]. All of these methods lead to state-of-the-art results.…”

Section: Introductionmentioning

confidence: 99%

Gabor feature based robust representation and classification for face recognition with Gabor occlusion dictionary

Yang

Zhang

Shiu

et al. 2013

Pattern Recognition

142

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By representing the input testing image as a sparse linear combination of the training samples via l 1 -norm minimization, sparse representation based classification (SRC) has shown promising results for face recognition (FR). Particularly, by introducing an identity occlusion dictionary to code the occluded portions of face images, SRC could lead to robust FR results against face occlusion. However, the l 1 -norm minimization and the high number of atoms in the identity occlusion dictionary make the SRC scheme computationally very expensive. In this paper, a Gabor feature based robust representation and classification (GRRC) scheme is proposed for robust FR. The use of Gabor features not only increases the discrimination power of face representation, but also allows us to compute a compact Gabor occlusion dictionary which has much less atoms than the identity occlusion dictionary. Furthermore, we show that with Gabor feature transformation, l 2 -norm could take place the role of l 1 -norm to regularize the coding coefficients, which reduces significantly the computational cost in coding occluded face images. Our extensive experiments on benchmark face databases, which have variations of lighting, expression, pose and occlusion, demonstrated the high effectiveness and efficiency of the proposed GRRC method.

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Passive Multimodal 2-D+3-D Face Recognition Using Gabor Features and Landmark Distances

Cited by 35 publications

References 49 publications

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

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

Persistent homology to analyse 3D faces and assess body weight gain

Gabor feature based robust representation and classification for face recognition with Gabor occlusion dictionary

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