Probabilistic fusion of regional scores in 3D face recognition

Erdoğmuş, Nesli; Daniel, Lionel; Dugelay, Jean‐Luc

doi:10.1109/icip.2012.6467145

Cited by 4 publications

(2 citation statements)

References 17 publications

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“…8 Different types of expressions gathered for subject 04514 and their associated texture and 3D images [69] proposed to firstly search for the nasal area in the center of the image, and then extract the outline of the diagonal area of the nasal area as a feature. Erdogmus et al [77] proposed another local feature based method. They divided the face into several parts, and then calculated the similarity of the corresponding parts between two 3D face images.…”

Section: Research On Expression-invariant 3d Face Recognitionmentioning

confidence: 99%

3D face recognition: a survey

Zhou

Xiao

2018

Hum. Cent. Comput. Inf. Sci.

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Face recognition has been a hot research area for its wide range of applications [1]. In human identification scenarios, facial metrics are more naturally accessible than many other biometrics, such as iris, fingerprint, and palm print [2]. Face recognition is also highly valuable in human computer interaction, access control, video surveillance, and many other applications. Although 2D face recognition research made significant progresses in recent years, its accuracy is still highly depended on light conditions and human poses [3, 4]. When the light is dim or the face poses are not properly aligned in the camera view, the recognition accuracy will suffer. The fast evolution of 3D sensors reveals a new path for face recognition that could overcome the fundamental limitations of 2D technologies. The geometric information contained in 3D facial data could substantially improve the recognition accuracy under conditions that are difficult for 2D technologies [5]. Many researchers have turned their focuses to 3D face recognition and made this research area a new trend. A general work flow for 3D face recognition is shown in Fig. 1. The work flow could be decomposed into two phases and five stages. In the training phase, 3D face data are acquired and then preprocessed to obtain "clean" 3D faces. Then the data are processed by feature extraction algorithms to find the features that could be used to differentiate faces. The features of each face are then stored into the feature database. In the testing phase, the target face goes through the acquisition, preprocessing, and feature extraction Abstract 3D face recognition has become a trending research direction in both industry and academia. It inherits advantages from traditional 2D face recognition, such as the natural recognition process and a wide range of applications. Moreover, 3D face recognition systems could accurately recognize human faces even under dim lights and with variant facial positions and expressions, in such conditions 2D face recognition systems would have immense difficulty to operate. This paper summarizes the history and the most recent progresses in 3D face recognition research domain. The frontier research results are introduced in three categories: pose-invariant recognition, expression-invariant recognition, and occlusion-invariant recognition. To promote future research, this paper collects information about publicly available 3D face databases. This paper also lists important open problems.

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Section: Research On Expression-invariant 3d Face Recognitionmentioning

confidence: 99%

3D face recognition: a survey

Zhou

Xiao

2018

Hum. Cent. Comput. Inf. Sci.

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“…Considerable research efforts have been devoted to handle expression problems by treating a human face as a rigid subject, because some facial regions always remain unchanged even under expression variations (Erdogmus et al, 2012;Bornak et al, 2010;Miao and Krim, 2011). Other approaches employ the deformation algorithms to recover the expression distortions by extracting local features (Bowyer et al, 2006;Samir et al, 2006;Li et al, 2011;Smeets et al, 2013; employ the ICP algorithm to match the probe image and the gallery image, they chose 28 regions from 38 facial areas as the best combination to achieve maximum matching performance.…”

Section: Introductionmentioning

confidence: 99%

Accumulating weighted segmentation in 3D face recognition

Wang¹,

Hu²,

Ju³

2022

IJWMC

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In this paper, an accumulating weighted face segmentation approach based on the rigid level of human facial areas is introduced. A mass of 3D face data is measured and analysed to define the most expression-invariant region. Different locations or regions on the human face are observed to have dissimilar invariant levels. Thus, an accumulating weight method is proposed to represent the rigid degree under expression variations. In face identification experiments, performance by employing the accumulating weight is demonstrated to be higher than methods using the expression-invariant region and the full face, respectively. This accumulating weighted face segmentation approach outperforms other state-of-the-art methods in 3D face recognition experiments.

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Combination of score level fusion methods in receiver operating characteristic space

Sedighi

Analoui

2017

2017 3rd International Conference on Pattern Recognition and Image Analysis (IPRIA)

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Probabilistic fusion of regional scores in 3D face recognition

Cited by 4 publications

References 17 publications

3D face recognition: a survey

3D face recognition: a survey

Accumulating weighted segmentation in 3D face recognition

Combination of score level fusion methods in receiver operating characteristic space

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