Recognition of Genetic Diseases Based on Combined Feature Extraction From 2D Face Images
Screening patients with genetic diseases using automated facial image analysis is an urgent task. A method for recognizing genetic syndromes from a frontal image of a face has been developed and studied. The classification was made to 8 syndromes (Angelman, Apert, Cornelia de Lange, Down, Fragile X,...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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FRUCT
2020-04-01
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| Series: | Proceedings of the XXth Conference of Open Innovations Association FRUCT |
| Subjects: | |
| Online Access: | https://www.fruct.org/publications/fruct26/files/Kum.pdf |
| Summary: | Screening patients with genetic diseases using automated facial image analysis is an urgent task. A method for recognizing genetic syndromes from a frontal image of a face has been developed and studied. The classification was made to 8 syndromes (Angelman, Apert, Cornelia de Lange, Down, Fragile X, Progeria, Treacher Collins, Williams). Various types of features were investigated: geometric and deep features. For facial points localization, 3D face reconstruction was used (using the Deep3DFaceReconstruction library). Sets of 68 and 35709 (all points of 3D reconstruction) points were investigated. The effect of reducing the dimension of the feature vector on the classification accuracy is also investigated. According to the results of 5-fold cross-validation, the best average recognition accuracy was 92.5 % (combined features, Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA) and logistic regression), which is comparable to the results in similar works. |
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| ISSN: | 2305-7254 2343-0737 |