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Journal articles on the topic '3D facial landmarking'

Author: Grafiati

Published: 3 June 2025

Last updated: 20 July 2025

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1

Bannister, Jordan J., Sebastian R. Crites, J. David Aponte, et al. "Fully Automatic Landmarking of Syndromic 3D Facial Surface Scans Using 2D Images." Sensors 20, no. 11 (2020): 3171. http://dx.doi.org/10.3390/s20113171.

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3D facial landmarks are known to be diagnostically relevant biometrics for many genetic syndromes. The objective of this study was to extend a state-of-the-art image-based 2D facial landmarking algorithm for the challenging task of 3D landmark identification on subjects with genetic syndromes, who often have moderate to severe facial dysmorphia. The automatic 3D facial landmarking algorithm presented here uses 2D image-based facial detection and landmarking models to identify 12 landmarks on 3D facial surface scans. The landmarking algorithm was evaluated using a test set of 444 facial scans w

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Križaj, Janez, Peter Peer, Vitomir Štruc, and Simon Dobrišek. "Simultaneous multi-descent regression and feature learning for facial landmarking in depth images." Neural Computing and Applications 32, no. 24 (2019): 17909–26. http://dx.doi.org/10.1007/s00521-019-04529-7.

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AbstractFace alignment (or facial landmarking) is an important task in many face-related applications, ranging from registration, tracking, and animation to higher-level classification problems such as face, expression, or attribute recognition. While several solutions have been presented in the literature for this task so far, reliably locating salient facial features across a wide range of posses still remains challenging. To address this issue, we propose in this paper a novel method for automatic facial landmark localization in 3D face data designed specifically to address appearance varia

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Kai, Wang, Jun An, Xi Zhao, and Jianhua Zou. "Accurate landmarking from 3D facial scans by CNN and cascade regression." International Journal of Wavelets, Multiresolution and Information Processing 16, no. 02 (2018): 1840007. http://dx.doi.org/10.1142/s0219691318400076.

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Facial landmarking locates the key facial feature points on facial data, which provides not only information on semantic facial structures, but also prior knowledge for other types of facial analysis. However, most of the existing works still focus on the 2D facial image which is quite sensitive to the lighting condition changes. In order to address this limitation, this paper proposed a coarse-to-fine method only based on the 3D facial scan data extracted from professional equipment to automatically and accurately estimate the landmark localization. Specifically, we firstly trained a convolut

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Vezzetti, Enrico, Domenico Speranza, Federica Marcolin, Giulia Fracastoro, and Giorgia Buscicchio. "EXPLOITING 3D ULTRASOUND FOR FETAL DIAGNOSTIC PURPOSE THROUGH FACIAL LANDMARKING." Image Analysis & Stereology 33, no. 3 (2014): 167. http://dx.doi.org/10.5566/ias.1100.

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In the last decade, three-dimensional landmarking has gained attention for different applications, such as face recognition for both identification of suspects and authentication, facial expression recognition, corrective and aesthetic surgery, syndrome study and diagnosis. This work focuses on the last one by proposing a geometrically-based landmark extraction algorithm aimed at diagnosing syndromes on babies before their birth. Pivotal role in this activity is the support provided by physicians and 3D ultrasound tools for working on real faces. In particular, the landmarking algorithm here p

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Wang, Kai, Xi Zhao, Wanshun Gao, and Jianhua Zou. "A Coarse-to-Fine Approach for 3D Facial Landmarking by Using Deep Feature Fusion." Symmetry 10, no. 8 (2018): 308. http://dx.doi.org/10.3390/sym10080308.

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Facial landmarking locates the key facial feature points on facial data, which provides not only information on semantic facial structures, but also prior knowledge for other kinds of facial analysis. However, most of the existing works still focus on the 2D facial image which may suffer from lighting condition variations. In order to address this limitation, this paper presents a coarse-to-fine approach to accurately and automatically locate the facial landmarks by using deep feature fusion on 3D facial geometry data. Specifically, the 3D data is converted to 2D attribute maps firstly. Then,

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6

de Jong, Markus A., Andreas Wollstein, Clifford Ruff, et al. "An Automatic 3D Facial Landmarking Algorithm Using 2D Gabor Wavelets." IEEE Transactions on Image Processing 25, no. 2 (2016): 580–88. http://dx.doi.org/10.1109/tip.2015.2496183.

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Sun, Jia, Di Huang, Yunhong Wang, and Liming Chen. "Expression Robust 3D Facial Landmarking via Progressive Coarse-to-Fine Tuning." ACM Transactions on Multimedia Computing, Communications, and Applications 15, no. 1 (2019): 1–23. http://dx.doi.org/10.1145/3282833.

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Vezzetti, Enrico, Domenico Speranza, Federica Marcolin, and Giulia Fracastoro. "DIAGNOSING CLEFT LIP PATHOLOGY IN 3D ULTRASOUND: A LANDMARKING-BASED APPROACH." Image Analysis & Stereology 35, no. 1 (2015): 53. http://dx.doi.org/10.5566/ias.1339.

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The aim of this work is to automatically diagnose and formalize prenatal cleft lip with representative key points and identify the type of defect (unilateral, bilateral, right, or left) in three-dimensional ultrasonography (3D US). Geometry has been used as a framework for describing facial shapes and curvatures. Then, descriptors coming from this field are employed for identifying the typical key points of the defect and its dimensions. The descriptive accuracy of these descriptors has allowed us to automatically extract reference points, quantitative distances, labial profiles, and to provid

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Agbolade, Olalekan, Azree Nazri, Razali Yaakob, Abdul Azim Abd Ghani, and Yoke Kqueen Cheah. "Landmark-based homologous multi-point warping approach to 3D facial recognition using multiple datasets." PeerJ Computer Science 6 (January 16, 2020): e249. http://dx.doi.org/10.7717/peerj-cs.249.

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Over the years, neuroscientists and psychophysicists have been asking whether data acquisition for facial analysis should be performed holistically or with local feature analysis. This has led to various advanced methods of face recognition being proposed, and especially techniques using facial landmarks. The current facial landmark methods in 3D involve a mathematically complex and time-consuming workflow involving semi-landmark sliding tasks. This paper proposes a homologous multi-point warping for 3D facial landmarking, which is verified experimentally on each of the target objects in a giv

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Alshehhi, Aamer, Mira Mousa, Guan K. Tay, Naoufel Werghi, and Habiba AlSafar. "Genome wide association study reveals novel associations with face morphology." PLOS ONE 20, no. 2 (2025): e0299660. https://doi.org/10.1371/journal.pone.0299660.

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Genome-wide association studies (GWAS) on the Middle Eastern population, including the United Arab Emirates (UAE), have been relatively limited. The present study aims to investigate genotype-face morphology associations in the UAE population through Genome Wide Association Studies (GWAS). Phenotypic data (44 face measurements) from 172 Emiratis was obtained through three-dimensional (3D) scanning technology and an automatic face landmarking technique. GWAS analysis revealed associations of 19 genetic loci with six face features, 14 of which are novel. The GWAS analysis revealed 11 significant

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Alshehhi, Aamer, Aliya Almarzooqi, Khadija Alhammadi, Naoufel Werghi, Guan K. Tay, and Habiba Alsafar. "Advancement in Human Face Prediction Using DNA." Genes 14, no. 1 (2023): 136. http://dx.doi.org/10.3390/genes14010136.

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The rapid improvements in identifying the genetic factors contributing to facial morphology have enabled the early identification of craniofacial syndromes. Similarly, this technology can be vital in forensic cases involving human identification from biological traces or human remains, especially when reference samples are not available in the deoxyribose nucleic acid (DNA) database. This review summarizes the currently used methods for predicting human phenotypes such as age, ancestry, pigmentation, and facial features based on genetic variations. To identify the facial features affected by D

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12

Dai, Hang, Nick Pears, William Smith, and Christian Duncan. "Statistical Modeling of Craniofacial Shape and Texture." International Journal of Computer Vision 128, no. 2 (2019): 547–71. http://dx.doi.org/10.1007/s11263-019-01260-7.

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Abstract We present a fully-automatic statistical 3D shape modeling approach and apply it to a large dataset of 3D images, the Headspace dataset, thus generating the first public shape-and-texture 3D morphable model (3DMM) of the full human head. Our approach is the first to employ a template that adapts to the dataset subject before dense morphing. This is fully automatic and achieved using 2D facial landmarking, projection to 3D shape, and mesh editing. In dense template morphing, we improve on the well-known Coherent Point Drift algorithm, by incorporating iterative data-sampling and alignm

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Crins-de Koning, Marjolein, Robin Bruggink, Marloes Nienhuijs, Till Wagner, Ewald M. Bronkhorst, and Edwin M. Ongkosuwito. "Three-dimensional analysis of facial morphology in nine-year-old children with different unilateral orofacial clefts compared to normative data." PeerJ 13 (January 21, 2025): e18739. https://doi.org/10.7717/peerj.18739.

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Aim To compare three-dimensional (3D) facial morphology of various unilateral cleft subphenotypes at 9-years of age to normative data using a general face template and automatic landmarking. The secondary objective is to compare facial morphology of 9-year-old children with unilateral fusion to differentiation defects. Methods 3D facial stereophotogrammetric images of 9-year-old unilateral cleft patients were imported into 3DMedX® for processing. All images of patients with a right sided cleft were mirrored. A regionalized general facial template was used for standardization. This template was

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14

Suryavanshi, Shrirang, Raju Ladhwe, and Omkar Joshi. "In Cabin Driver Monitoring and Alerting System For Passenger cars using Machine Learning." Journal of Physics: Conference Series 2601, no. 1 (2023): 012040. http://dx.doi.org/10.1088/1742-6596/2601/1/012040.

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Abstract The primary causes of road accidents are driver errors. Such as excessive speeding, driver distraction, and driver sleepiness. Diver sleepiness and distraction can be controlled by implementing advanced technology such as an in-cabin driver monitoring system. This is a technological solution that uses a machine learning algorithm to improve road safety. This paper describes a new real-time driver monitoring and alerting system that solely monitors driver Sleepiness, diver distraction, and seatbelt wearing status in order to prevent road accidents. It employs the YOLOv5 deep learning a

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15

de Jong, Markus A., Pirro Hysi, Tim Spector, et al. "Ensemble landmarking of 3D facial surface scans." Scientific Reports 8, no. 1 (2018). http://dx.doi.org/10.1038/s41598-017-18294-x.

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16

Al-baker, Bodore, Xiangyang Ju, Peter Mossey, and Ashraf Ayoub. "The accuracy of automated facial landmarking - a comparative study between Cliniface software and patch-based Convoluted Neural Network algorithm." European Journal of Orthodontics 47, no. 2 (2025). https://doi.org/10.1093/ejo/cjaf009.

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Abstract Background Automatic landmarking software packages simplify the analysis of the 3D facial images. Their main deficiency is the limited accuracy of detecting landmarks for routine clinical applications. Cliniface is readily available open-access software for automatic facial landmarking, its validity has not been fully investigated. Objectives Evaluate the accuracy of Cliniface software in comparison with the developed patch-based Convoluted Neural Network (CNN) algorithm in identifying facial landmarks. Materials /Methods The study was carried out on 30 3D photographic images; twenty

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17

Berends, Bo, Freek Bielevelt, Ruud Schreurs, Shankeeth Vinayahalingam, Thomas Maal, and Guido de Jong. "Fully automated landmarking and facial segmentation on 3D photographs." Scientific Reports 14, no. 1 (2024). http://dx.doi.org/10.1038/s41598-024-56956-9.

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AbstractThree-dimensional facial stereophotogrammetry provides a detailed representation of craniofacial soft tissue without the use of ionizing radiation. While manual annotation of landmarks serves as the current gold standard for cephalometric analysis, it is a time-consuming process and is prone to human error. The aim in this study was to develop and evaluate an automated cephalometric annotation method using a deep learning-based approach. Ten landmarks were manually annotated on 2897 3D facial photographs. The automated landmarking workflow involved two successive DiffusionNet models. T

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18

Daniel, Chen, Mamic George, Fookes Clinton, and Sridharan Sridha. "Scale-Space Volume Descriptors for Automatic 3D Facial Feature Extraction." November 27, 2009. https://doi.org/10.5281/zenodo.1055711.

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An automatic method for the extraction of feature points for face based applications is proposed. The system is based upon volumetric feature descriptors, which in this paper has been extended to incorporate scale space. The method is robust to noise and has the ability to extract local and holistic features simultaneously from faces stored in a database. Extracted features are stable over a range of faces, with results indicating that in terms of intra-ID variability, the technique has the ability to outperform manual landmarking.

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19

Yang, Yuyan, Mengyuan Zhang, Yicheng An, et al. "Automated 3D Perioral Landmark Detection Using High-Resolution Network: Artificial Intelligence-Based Anthropometric Analysis." Aesthetic Surgery Journal, April 25, 2024. http://dx.doi.org/10.1093/asj/sjae103.

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Abstract Background 3D facial stereophotogrammetry, as a convenient, non-invasive and highly reliable evaluation tool, has shown great potential in pre-operative planning and treatment efficacy evaluation of plastic surgery in recent years. However, it requires manual identification of facial landmarks by trained evaluators to obtain anthropometric data, which consumes large amount of time and effort. Automatic 3D facial landmark localization may facilitate fast data acquisition and eliminate evaluator error. Objectives In this paper, we propose a novel deep-learning method based on dimension-

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20

Wilke, Franziska, Harold Matthews, Noah Herrick, Nichole Dopkins, Peter Claes, and Susan Walsh. "A novel approach to craniofacial analysis using automated 3D landmarking of the skull." Scientific Reports 14, no. 1 (2024). http://dx.doi.org/10.1038/s41598-024-63137-1.

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AbstractAutomatic dense 3D surface registration is a powerful technique for comprehensive 3D shape analysis that has found a successful application in human craniofacial morphology research, particularly within the mandibular and cranial vault regions. However, a notable gap exists when exploring the frontal aspect of the human skull, largely due to the intricate and unique nature of its cranial anatomy. To better examine this region, this study introduces a simplified single-surface craniofacial bone mask comprising of 6707 quasi-landmarks, which can aid in the classification and quantificati

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21

Ridel, Alison F., Fabrice Demeter, Ericka N. L’Abbé, Dirk Vandermeulen, and Anna C. Oettlé. "Shape analysis of the nasal complex among South African groups from CBCT scans." South African Journal of Science 120, no. 5/6 (2024). http://dx.doi.org/10.17159/sajs.2024/12972.

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Three-dimensional (3D) anatomical extraction techniques could help the forensic anthropologist in a precise and inclusive assessment of biological phenotypes for the development of facial reconstruction methods. In this research, the nose morphology and the underlying hard tissue of two South African populations were studied. To this end, a 3D computer-assisted approach based on an automated landmarking workflow was used to generate relevant 3D anatomical components, and shape discrepancies were investigated using a data set of 200 cone-beam computer tomography (CBCT) scans. The anatomical lan

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Qiao, Hui, Jingze Tan, Shaoqing Wen, Menghan Zhang, Shuhua Xu, and Li Jin. "De Novo Dissecting the Three-Dimensional Facial Morphology of 2379 Han Chinese Individuals." Phenomics, June 8, 2023. http://dx.doi.org/10.1007/s43657-023-00109-x.

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AbstractPhenotypic diversity, especially that of facial morphology, has not been fully investigated in the Han Chinese, which is the largest ethnic group in the world. In this study, we systematically analyzed a total of 14,838 facial traits representing 15 categories with both a large-scale three-dimensional (3D) manual landmarking database and computer-aided facial segmented phenotyping in 2379 Han Chinese individuals. Our results illustrate that homogeneous and heterogeneous facial morphological traits exist among Han Chinese populations across the three geographical regions: Zhengzhou, Tai

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23

Al-baker, Bodore, Ashraf Ayoub, Xiangyang Ju, and Peter Mossey. "Patch-based convolutional neural networks for automatic landmark detection of 3D facial images in clinical settings." European Journal of Orthodontics 46, no. 6 (2024). http://dx.doi.org/10.1093/ejo/cjae056.

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Abstract Background The facial landmark annotation of 3D facial images is crucial in clinical orthodontics and orthognathic surgeries for accurate diagnosis and treatment planning. While manual landmarking has traditionally been the gold standard, it is labour-intensive and prone to variability. Objective This study presents a framework for automated landmark detection in 3D facial images within a clinical context, using convolutional neural networks (CNNs), and it assesses its accuracy in comparison to that of ground-truth data. Material and methods Initially, an in-house dataset of 408 3D fa

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