Dense 3D Face Correspondence

Gilani, Syed Zulqarnain; Mian, Ajmal; Shafait, Faisal; Reid, Ian

doi:10.1109/tpami.2017.2725279

Cited by 67 publications

(26 citation statements)

References 83 publications

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“…The landmarks also identified the midline on the face. After preprocessing, dense correspondence [Gilani, Mian, Shafait, & Reid, 2018] was established among all 3D faces. This is a process where thousands of 3D points across two or more faces are mapped to each other such that there is a one-to-one correspondence between them.…”

Section: Fa Calculationsmentioning

confidence: 99%

Increased facial asymmetry in autism spectrum conditions is associated with symptom presentation

et al. 2019

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A key research priority in the study of autism spectrum conditions (ASC) is the discovery of biological markers that may help to identify and elucidate etiologically distinct subgroups. One physical marker that has received increasing research attention is facial structure. Although there remains little consensus in the field, findings relating to greater facial asymmetry (FA) in ASC exhibit some consistency. As there is growing recognition of the importance of replicatory studies in ASC research, the aim of this study was to investigate the replicability of increased FA in autistic children compared to nonautistic peers. Using three‐dimensional photogrammetry, this study examined FA in 84 autistic children, 110 typically developing children with no family history of the condition, and 49 full siblings of autistic children. In support of previous literature, significantly greater depth‐wise FA was identified in autistic children relative to the two comparison groups. As a further investigation, increased lateral FA in autistic children was found to be associated with greater severity of ASC symptoms on the Autism Diagnostic Observation Schedule, second edition, specifically related to repetitive and restrictive behaviors. These outcomes provide an important and independent replication of increased FA in ASC, as well as a novel contribution to the field. Having confirmed the direction and areas of increased FA in ASC, these findings could motivate a search for potential underlying brain dysmorphogenesis. Autism Res 2019, 12: 1774–1783. © 2019 International Society for Autism Research, Wiley Periodicals, Inc. Lay Summary This study looked at the amount of facial asymmetry (FA) in autistic children compared to typically developing children and children who have siblings with autism. The study found that autistic children, compared to the other two groups, had greater FA, and that increased FA was related to greater severity of autistic symptoms. The face and brain grow together during the earliest stages of development, and so findings of facial differences in autism might inform future studies of early brain differences associated with the condition.

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Section: Fa Calculationsmentioning

confidence: 99%

Increased facial asymmetry in autism spectrum conditions is associated with symptom presentation

et al. 2019

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“…Gilani et al . 10 ), this is achieved by gradually warping a generic template composed of thousands of points into the shape of each target image. The most common and simple algorithm, the iterative closest point (ICP), transforms the template shape rigidly towards the target by imposing each point on the template to its closet point on the target 11 .…”

Section: Introductionmentioning

confidence: 99%

“…Dense landmark configurations also provide a more comprehensive approximation of the form and are more suitable for applications that require synthesis of a recognizable instance of the actual structure, such as predicting a complete shape from DNA 19 , synthetic growth and ageing of a face 20,21 , constructing 3D facial composites for forensic applications 22 , and characterization of dysmorphology for clinical assessment 23,24 . Computer scientists are consistently developing and improving algorithms for the identification of landmark points on 3D image data 10,16,17,25–34 , but fewer of these developments have been utilized in biological studies of morphology 18,35–43 , and more rare is quantifying morphology on a dense scale 10,16–18,34,37 . Those that have been developed are often done so in-house and siloed.…”

Section: Introductionmentioning

confidence: 99%

MeshMonk: Open-source large-scale intensive 3D phenotyping

White

Ortega-Castrillón

Matthews

et al. 2019

Sci Rep

109

131

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Dense surface registration, commonly used in computer science, could aid the biological sciences in accurate and comprehensive quantification of biological phenotypes. However, few toolboxes exist that are openly available, non-expert friendly, and validated in a way relevant to biologists. Here, we report a customizable toolbox for reproducible high-throughput dense phenotyping of 3D images, specifically geared towards biological use. Given a target image, a template is first oriented, repositioned, and scaled to the target during a scaled rigid registration step, then transformed further to fit the specific shape of the target using a non-rigid transformation. As validation, we use n = 41 3D facial images to demonstrate that the MeshMonk registration is accurate, with 1.26 mm average error, across 19 landmarks, between placements from manual observers and using the MeshMonk toolbox. We also report no variation in landmark position or centroid size significantly attributable to landmarking method used. Though validated using 19 landmarks, the MeshMonk toolbox produces a dense mesh of vertices across the entire surface, thus facilitating more comprehensive investigations of 3D shape variation. This expansion opens up exciting avenues of study in assessing biological shapes to better understand their phenotypic variation, genetic and developmental underpinnings, and evolutionary history.

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“…For example, the famous Bosphorus dataset contains around 4k images and BU-3DFE contains around 2.5k (see Table 4). It is therefore clear that the performance of 3D facial recognition is below the expectations of its promoters even if some authors [165][166][167][168] obtain relatively acceptable performance but at the cost of complications and rather confused preprocessing. In the database side, we can note that the academic community has plenty of large-scale 2D facial databases.…”

Section: Introduction To 3d Face Recognitionmentioning

confidence: 99%

Past, Present, and Future of Face Recognition: A Review

Adjabi¹,

Ouahabi²,

Benzaoui³

et al. 2020

Preprint

107

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Face recognition is one of the most active research fields of computer vision and pattern recognition, with many practical and commercial applications including identification, access control, forensics, and human-computer interactions. However, identifying a face in a crowd raises serious questions about individual freedoms and poses ethical issues. Significant methods, algorithms, approaches, and databases have been proposed over recent years to study constrained and unconstrained face recognition. 2D approaches reached some degree of maturity and reported very high rates of recognition. This performance is achieved in controlled environments where the acquisition parameters are controlled, such as lighting, angle of view, and distance between the camera-subject. However, if the ambient conditions (e.g., lighting) or the facial appearance (e.g., pose or facial expression) change, this performance will degrade dramatically. 3D approaches were proposed as an alternative solution to the problems mentioned above. The advantage of 3D data lies in its invariance to pose and lighting conditions, which has enhanced recognition systems efficiency. 3D data, however, is somewhat sensitive to changes in facial expressions. This review presents the history of face recognition technology, the current state-of-the-art methodologies, and future directions. We specifically concentrate on the most recent databases, 2D and 3D face recognition methods. Besides, we pay particular attention to deep learning approach as it presents the actuality in this field. Open issues are examined and potential directions for research in facial recognition are proposed in order to provide the reader with a point of reference for topics that deserve consideration.

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Dense 3D Face Correspondence

Cited by 67 publications

References 83 publications

Increased facial asymmetry in autism spectrum conditions is associated with symptom presentation

Increased facial asymmetry in autism spectrum conditions is associated with symptom presentation

MeshMonk: Open-source large-scale intensive 3D phenotyping

Past, Present, and Future of Face Recognition: A Review

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