Computer-based recognition of dysmorphic faces

Loos, Hartmut S.; Wieczorek, Dagmar; Würtz, Rolf P.; Malsburg, Christoph von der; Horsthemke, Bernhard

doi:10.1038/sj.ejhg.5200997

Cited by 76 publications

(68 citation statements)

References 16 publications

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“…(14) reported on performing face classification using only texture analysis via Gabor wavelet transformations and various classifiers. The data sets consisted initially of 55 patients suffering from five genetic syndromes, and later of 147 patients suffering from ten genetic syndromes, achieving an overall classification accuracy of 76 and 75.7%, respectively (13,14). In a study published in 2008, Vollmar et al (15) included side-view photographs and geometric information in the face classification analyses, resulting in improved accuracy in an even larger data set (14 genetic syndromes).…”

Section: Application In Cs and Current Researchmentioning

confidence: 99%

Diagnostic use of facial image analysis software in endocrine and genetic disorders: review, current results and future perspectives

Kosilek

Frohner

Würtz

et al. 2015

European Journal of Endocrinology

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Cushing's syndrome (CS) and acromegaly are endocrine diseases that are currently diagnosed with a delay of several years from disease onset. Novel diagnostic approaches and increased awareness among physicians are needed. Face classification technology has recently been introduced as a promising diagnostic tool for CS and acromegaly in pilot studies. It has also been used to classify various genetic syndromes using regular facial photographs. The authors provide a basic explanation of the technology, review available literature regarding its use in a medical setting, and discuss possible future developments. The method the authors have employed in previous studies uses standardized frontal and profile facial photographs for classification. Image analysis is based on applying mathematical functions evaluating geometry and image texture to a grid of nodes semi-automatically placed on relevant facial structures, yielding a binary classification result. Ongoing research focuses on improving diagnostic algorithms of this method and bringing it closer to clinical use. Regarding future perspectives, the authors propose an online interface that facilitates submission of patient data for analysis and retrieval of results as a possible model for clinical application.

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Section: Application In Cs and Current Researchmentioning

confidence: 99%

Diagnostic use of facial image analysis software in endocrine and genetic disorders: review, current results and future perspectives

Kosilek

Frohner

Würtz

et al. 2015

European Journal of Endocrinology

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“…Classification based on 2D images of face shape alone achieved a recognition rate for fragile X syndrome between 75 and 99.9%, depending on the technique used. 22,23 The overall syndrome recognition rate of 10 different syndromes ranged between 52 and 76%, confirming that computer-based methods are able to recognize some syndromerelated facial characteristics better than others. Studies using DSMs of 3D face shape have delineated common facial features in a range of neurodevelopmental conditions, often, in addition, establishing accurate discriminating characteristics or assisting the determination of phenotype-genotype correlations.…”

Section: Discussionmentioning

confidence: 75%

Craniofacial characteristics of fragile X syndrome in mouse and man

Heulens

Suttie²,

Постнов

et al. 2012

Eur J Hum Genet

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For a disorder as common as fragile X syndrome, the most common hereditary form of cognitive impairment, the facial features are relatively ill defined. An elongated face and prominent ears are the most commonly accepted dysmorphic hallmarks. We analysed 3D facial photographs of 51 males and 15 females with full FMR1 mutations and 9 females with a premutation using dense-surface modelling techniques and a new technique that forms a directed graph with normalized face shapes as nodes and edges linking those with closest dysmorphism. In addition to reconfirming known features, we confirmed the occurrence of some at an earlier age than previously recorded. We also identified as yet unrecorded facial characteristics such as reduced facial depth, hypoplasticity of the nasal bone-cartilage interface and narrow mid-facial width exaggerating ear prominence. As no consistent craniofacial abnormalities had been reported in animal models, we analysed micro-CT images of the fragile X mouse model. Results indicated altered dimensions in the mandible and both outer and inner skull, with the latter potentially reflecting differences in neuroanatomy. We extrapolated the mouse results to face shape differences of the human fragile X face.

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“…16,25 Recently, the data format has been applied successfully to the classification of different syndromes, which influence the facial appearance, from static facial images. 5,10,12,15,18,21,23 To better interpret which kind of features are contained in the Gabor graphs and for inspection by the clinician, it is important to visualize Gabor graphs by reconstructing images from them.…”

Section: Introductionmentioning

confidence: 99%

Reconstruction of images from Gabor graphs with applications in facial image processing

Günther

Böhringer

Wieczorek

et al. 2015

Int. J. Wavelets Multiresolut Inf. Process.

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Graphs labeled with complex-valued Gabor jets are one of the important data formats for face recognition and the classification of facial images into medically relevant classes like genetic syndromes. We here present an interpolation rule and an iterative algorithm for the reconstruction of images from these graphs. This is especially important if graphs have been manipulated for information processing. One such manipulation is averaging the graphs of a single syndrome, another one building a composite face from the features of various individuals. In reconstructions of averaged graphs of genetic syndromes, the patients' identities are suppressed, while the properties of the syndromes are emphasized. These reconstructions from average graphs have a much better quality than averaged images.

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Computer-based recognition of dysmorphic faces

Cited by 76 publications

References 16 publications

Diagnostic use of facial image analysis software in endocrine and genetic disorders: review, current results and future perspectives

Diagnostic use of facial image analysis software in endocrine and genetic disorders: review, current results and future perspectives

Craniofacial characteristics of fragile X syndrome in mouse and man

Reconstruction of images from Gabor graphs with applications in facial image processing

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