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Classification of Facial Images to Assist in the Diagnosis of Autism Spectrum Disorder: A Study on the Effect of Face Detection and Landmark Identification Algorithms

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Intelligent Systems (BRACIS 2023)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 14196))

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Abstract

Since facial morphology can be linked to brain developmental problems, studies have been conducted to develop computational systems to assist in the diagnosis of some neurodevelopmental disorders based on facial images. The first steps usually include face detection and landmark identification. Although there are several libraries that implement different algorithms for these tasks, to the best of our knowledge no study has discussed the effect of choosing these ready-to-use implementations on the performance of the final classifier. This paper compares four libraries for facial detection and landmark identification in the context of classification of facial images for computer-aided diagnosis of Autism Spectrum Disorder, where the classifiers achieved 0.92, the highest F1-score. The results indicate that the choice of which facial detection and landmark identification algorithms to use do in fact affect the final classifier performance. It appears that the causes are related to not only the quality of face and landmark identification, but also to the success rate of face detection. This last issue is particularly important when the initial training sample size is modest, which is usually the case in terms of classification of some syndromes or neurodevelopmental disorders based on facial images.

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References

  1. Face recognition database (2005). http://cbcl.mit.edu/software-datasets/heisele/facerecognition-database.html

  2. Aldridge, K., et al.: Facial phenotypes in subgroups of prepubertal boys with autism spectrum disorders are correlated with clinical phenotypes. Mol. Autism 2(1), 15 (2011)

    Article  MathSciNet  Google Scholar 

  3. Association, A.P., et al.: Diagnostic and statistical manual of mental disorders (DSM-5®). American Psychiatric Pub (2013)

    Google Scholar 

  4. Baltrusaitis, T., Robinson, P., Morency, L.P.: Constrained local neural fields for robust facial landmark detection in the wild. In: Proceedings of the IEEE International Conference on Computer Vision Workshops, pp. 354–361 (2013)

    Google Scholar 

  5. Bazarevsky, V., Kartynnik, Y., Vakunov, A., Raveendran, K., Grundmann, M.: BlazeFace: sub-millisecond neural face detection on mobile GPUs. CoRR abs/1907.05047 (2019)

    Google Scholar 

  6. Boehringer, S., et al.: Syndrome identification based on 2D analysis software. Eur. J. Hum. Genet. 14(10), 1082–1089 (2006)

    Article  Google Scholar 

  7. Bradski, G.: Opencv library. Dr. Dobb’s Journal of Software Tools (2000)

    Google Scholar 

  8. Canny, J.: A computational approach to edge detection. IEEE Trans. Pattern Anal. Mach. Intell. 6, 679–698 (1986)

    Article  Google Scholar 

  9. DeMyer, W., Zeman, W., Palmer, C.G.: The face predicts the brain: diagnostic significance of median facial anomalies for holoprosencephaly (arhinencephaly). Pediatrics 34(2), 256–263 (1964)

    Article  Google Scholar 

  10. Deth, R., Muratore, C., Benzecry, J., Power-Charnitsky, V.A., Waly, M.: How environmental and genetic factors combine to cause autism: a redox/methylation hypothesis. Neurotoxicology 29(1), 190–201 (2008)

    Article  Google Scholar 

  11. Farkas, L.G.: Anthropometry of the Head and Face. Raven Pr (1994)

    Google Scholar 

  12. Gilani, S.Z., et al.: Sexually dimorphic facial features vary according to level of autistic-like traits in the general population. J. Neurodev. Disord. 7(1), 14 (2015)

    Article  Google Scholar 

  13. Hammond, P., et al.: 3D analysis of facial morphology. Am. J. Med. Genet. A 126(4), 339–348 (2004)

    Article  Google Scholar 

  14. Johnson, C.P., Myers, S.M.: Identification and evaluation of children with autism spectrum disorders. Pediatrics 120(5), 1183–1215 (2007)

    Article  Google Scholar 

  15. King, D.E.: Dlib-ml: a machine learning toolkit. J. Mach. Learn. Res. 10, 1755–1758 (2009)

    Google Scholar 

  16. Kumov, V., Samorodov, A.: Recognition of genetic diseases based on combined feature extraction from 2D face images. In: 26th Conference of Open Innovations Association (FRUCT). IEEE (2020)

    Google Scholar 

  17. Lemaître, G., Nogueira, F., Aridas, C.K.: Imbalanced-learn: a python toolbox to tackle the curse of imbalanced datasets in machine learning. J. Mach. Learn. Res. 18(17), 1–5 (2017)

    Google Scholar 

  18. Levy, S.E., Mandell, D.S., Schultz, R.T.: Autism 374, 1627–1638 (2009)

    Google Scholar 

  19. Li, J., et al.: Feature selection: a data perspective. arXiv:1601.07996 (2016)

  20. Lord, C., Cook, E.H., Leventhal, B.L., Amaral, D.G.: Autism spectrum disorders. Neuron 28(2), 355–363 (2000)

    Article  Google Scholar 

  21. Lugaresi, C., et al.: MediaPipe: a framework for perceiving and processing reality. In: Third Workshop on Computer Vision for AR/VR at IEEE Computer Vision and Pattern Recognition (CVPR) 2019 (2019)

    Google Scholar 

  22. Mandell, D.S., Novak, M.M., Zubritsky, C.D.: Factors associated with age of diagnosis among children with autism spectrum disorders. Pediatrics 116(6), 1480–1486 (2005)

    Article  Google Scholar 

  23. Miles, J., Hadden, L., Takahashi, T., Hillman, R.: Head circumference is an independent clinical finding associated with autism. Am. J. Med. Genet. 95(4), 339–350 (2000)

    Article  Google Scholar 

  24. Muhle, R., Trentacoste, S.V., Rapin, I.: The genetics of autism. Pediatrics 113(5), e472–e486 (2004)

    Article  Google Scholar 

  25. Chawla, N.V., Bowyer, K.W., Lawrence, O.H., Philip Kegelmeyer, W.: Smote: synthetic minority over-sampling technique. J. Artif. Intell. Res. 16, 321–357 (2011)

    Article  MATH  Google Scholar 

  26. Obafemi-Ajayi, T., et al.: Facial structure analysis separates autism spectrum disorders into meaningful clinical subgroups. J. Autism Dev. Disord. 45(5), 1302–1317 (2015)

    Article  Google Scholar 

  27. Ozgen, H., et al.: Morphological features in children with autism spectrum disorders: a matched case-control study. J. Autism Dev. Disord. 41(1), 23–31 (2011)

    Article  Google Scholar 

  28. Pedregosa, F., et al.: Scikit-learn: machine learning in Python. J. Mach. Learn. Res. 12, 2825–2830 (2011)

    MathSciNet  MATH  Google Scholar 

  29. Rodier, P.M., Bryson, S.E., Welch, J.P.: Minor malformations and physical measurements in autism: data from Nova Scotia. Teratology 55(5), 319–325 (1997)

    Article  Google Scholar 

  30. Suzuki, S.: Topological structural analysis of digitized binary images by border following. Comput. Vis. Graph. Image Process. 30(1), 32–46 (1985)

    Article  MATH  Google Scholar 

  31. Weinberg, S.M., Naidoo, S., Govier, D.P., Martin, R.A., Kane, A.A., Marazita, M.L.: Anthropometric precision and accuracy of digital three-dimensional photogrammetry: comparing the genex and 3dMD imaging systems with one another and with direct anthropometry. J. Craniofac. Surg. 17(3), 477–483 (2006)

    Article  Google Scholar 

  32. Zhao, Q., et al.: Digital facial dysmorphology for genetic screening: hierarchical constrained local model using ICA. Med. Image Anal. 18(5), 699–710 (2014)

    Article  Google Scholar 

  33. Zhao, Q., Yao, G., Akhtar, F., Li, J., Pei, Y.: An automated approach to diagnose turner syndrome using ensemble learning methods. IEEE Access 8, 223335–223345 (2020)

    Article  Google Scholar 

Download references

Acknowledgments

We thank the patients and their families that allowed the execution of this research, as well as the team of the Autism Spectrum Program of the Clinics Hospital (PROTEA-HC). This study was funded by Brazilian National Council of Scientific and Technological Development, (CNPq) (grants 309330/2018-1, 157535/2017-7 and 309030/2019-6) and Scientific and Technological Initiation Program at University of Sao Paulo (PIBIC/PIBIT-CNPq/USP 2020/2021), the São Paulo Research Foundation (FAPESP) - National Institute of Science and Technology - Medicine Assisted by Scientific Computing (INCT-MACC) - grant 2014/50889-7, Sao Paulo Research Foundation (FAPESP) grants #2017/12646-3, #2020/01992-0, Coordenação de Aperfeiçoamento de Pessoal de Nível Superior – Brasil (CAPES), Dean’s Office for Research of the University of São Paulo (PRP-USP, grant 18.5.245.86.7) and the National Health Support Program for People with Disabilities (PRONAS/PCD) grant 25000.002484/2017-17.

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Authors and Affiliations

  1. School of Arts, Sciences and Humanities, University of Sao Paulo, R. Arlindo Béttio, 1000 - Ermelino Matarazzo, Sao Paulo-SP, 03828-000, Brazil

    Gabriel C. Michelassi, Henrique S. Bortoletti, Tuany D. Pinheiro, Thiago Nobayashi, Fabio R. D. de Barros, Rafael L. Testa, Fatima L. S. Nunes & Ariane Machado-Lima

  2. Department of Psychiatry, School of Medicine, University of Sao Paulo, R. Dr. Ovídio Pires de Campos, 785, Sao Paulo-SP, 05403-903, Brazil

    Andréia F. Silva, Mirian C. Revers, Joana Portolese & Helena Brentani

  3. Institute of Computing, University of Campinas, Av. Albert Einstein, 1251 - Cidade Universitária, Campinas-SP, 13083-852, Brazil

    Helio Pedrini

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  1. Gabriel C. Michelassi
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Correspondence to Ariane Machado-Lima .

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Editors and Affiliations

  1. Federal University of São Carlos, São Carlos, Brazil

    Murilo C. Naldi

  2. Centro Universitario da FEI, São Bernardo do Campo, Brazil

    Reinaldo A. C. Bianchi

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Michelassi, G.C. et al. (2023). Classification of Facial Images to Assist in the Diagnosis of Autism Spectrum Disorder: A Study on the Effect of Face Detection and Landmark Identification Algorithms. In: Naldi, M.C., Bianchi, R.A.C. (eds) Intelligent Systems. BRACIS 2023. Lecture Notes in Computer Science(), vol 14196. Springer, Cham. https://doi.org/10.1007/978-3-031-45389-2_18

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  • DOI: https://doi.org/10.1007/978-3-031-45389-2_18

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-45388-5

  • Online ISBN: 978-3-031-45389-2

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