Precision, repeatability and validation of indirect 3D anthropometric measurements with light-based imaging techniques

Enciso, Reyes; Alexandroni, Emanuel S.; Benyamein, K.; Keim, R.A.; Mah, James

doi:10.1109/isbi.2004.1398739

Cited by 7 publications

(8 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the recent time, 2D photographs are commonly used for such studies and for assessment and treatment of facial abnormalities. However, the 2D images in assessing facial morphology is subject to errors because the natural 3D facial anatomy will be translated in 2D (Enciso et al., 2004). The latest 3D imaging devices are specifically designed to capture and quantify facial morphology and are presently very common in the research environments (Da Silveira et al., 2003; Fourie, 2010; Plooij et al., 2011; Knoops et al., 2017).…”

Section: Discussionmentioning

confidence: 99%

“…Similarly, automatic method for the extraction of facial morphological features was recently devised from the 3D facial scans with 97% face classification accuracy (Abbas et al., 2015), thereby simplifying the whole process. Comparison between 2 methods can be used to determine the performance evaluation of technical equipment (Enciso et al., 2004; Aung et al., 1995, Aung et al., 2000).…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A comparison between 2D and 3D methods of quantifying facial morphology

Anas

Bamgbose

Nuhu

2019

Heliyon

View full text Add to dashboard Cite

Objectives Currently, two & three-dimensional (2D & 3D) imaging techniques have largely replaced the direct anthropometric method in the assessment of facial morphology, but the difference between the two techniques was not quantified. Therefore, the aim of this study was to compare and quantify (the difference between) the two techniques. Materials and methods The faces of 150 subjects (75 males, 75 females) of northern Nigeria, predominantly Hausa ethnic group, were photographed (using digital camera) and scanned (using a 3D surface laser scanner). Facial dimensions were generated from the resulting virtual 2D and 3D models. Data were analyzed using R-statistic software & Paired sample t-test/Pearson correlation were conducted to compare the two methods and to quantify the level of closeness between the two measurements. Results Intraclass correlation coefficient (ICC) was very low (0.26) for the 3D and 2D measurements indicating the level of differences between the methods. Measurements taken with laser scanner were higher relative to the one taken by camera. The mean differences between the 3D and the 2D methods of quantifying facial morphology indicated a statistically significant positive difference. CONCLUSION: 2D and 3D anthropometry cannot be used interchangeably since there exists statistically significant variation between the two methods.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A comparison between 2D and 3D methods of quantifying facial morphology

Anas

Bamgbose

Nuhu

2019

Heliyon

View full text Add to dashboard Cite

show abstract

“…Anthropometric landmarks have been located using computer-based systems, Enciso et al (2004), Deng and Neumann (2008) and Godil (2009) are some examples. Enciso et al (2004) validated a light-based imagining system with a 3D digitiser using a plastic mannequin head, pre-labelled with a subset of Farkas's anthropometric landmarks.…”

Section: Anthropometric Landmark Localisationmentioning

confidence: 99%

“…Enciso et al (2004) validated a light-based imagining system with a 3D digitiser using a plastic mannequin head, pre-labelled with a subset of Farkas's anthropometric landmarks.…”

Section: Anthropometric Landmark Localisationmentioning

confidence: 99%

Landmark Localisation in 3D Face Data

Romero

Pears

2009

2009 Sixth IEEE International Conference on Advanced Video and Signal Based Surveillance

View full text Add to dashboard Cite

Accurate landmark localisation is an essential precursor to many 3D face processing algorithms but, as yet, there is a lack of convincing solutions that work well over a wide range of head poses.In this thesis, an investigation to localise facial landmarks from 3D images is presented, without using any assumption concerning facial pose. In particular, this research devises new surface descriptors, which are derived from either unstructured face data, or a radial basis function (RBF) model of the facial surface.A ground-truth of eleven facial landmarks is collected over well-registered facial images in the Face Recognition Grand Challenge (FRGC) database. Then, a range of feature descriptors of varying complexity are investigated to illustrate repeatability and accuracy when computed for the full set of eleven facial landmarks. At this stage, the nose-tip and two inner-eye corners are observed as the most distinctive facial landmarks as a tradeoff among repeatability, accuracy, and complexity. Thus, this investigation focuses on the localisation of these three facial landmarks, which is the minimum number of landmarks necessary for pose normalisation.Two new families of descriptors are introduced, namely point-pair and point-triplet descriptors, which require two and three vertices respectively for their computation. Also, two facial landmark localisation methods are investigated; in the first, a binary decision tree is used to implement a cascade filter, in the second, graph matching is implemented via relaxation by elimination. Then, using all of these descriptors and algorithms, a number of systems are designed to localise the nose-tip and two inner-eye corners. Above all, 99.92% of nose-tip landmarks within an accuracy of 12 mm is the best localisation performance, which is achieved by one cascade filter system. Finally, landmark localisation performance is reported by using a novel cumulative error curve. Localisation results are gathered by computing errors of estimated landmark locations against respective ground-truth data.2

show abstract

“…Many papers have been published on the accuracy and precision of scan-derived measurements. Scan-derived measurements have been evaluated in terms of comparability with measurements obtained from traditional methods Paquette et al, 2000;Weinberg et al, 2004Weinberg et al, , 2006Daniell, 2008;Wong et al, 2008) or from CT images , repeatability of scan-derived body dimensions Enciso et al, 2004;Weinberg et al, 2004Weinberg et al, , 2006Kouchi and Mochimaru, 2008;Wong et al, 2008), and repeatability of scan-derived landmark locations obtained from the same image Kouchi and Mochimaru, 2008). However, quality parameters are not consistent among these studies.…”

Section: Introductionmentioning

confidence: 99%