Gauging Facial Abnormality Using Haar-Cascade Object Detector. Academic Article uri icon


  • The overriding clinical and academic challenge that inspires this work is the lack of a universally accepted, objective, and feasible method of measuring facial deformity; and, by extension, the lack of a reliable means of assessing the benefits and shortcomings of craniofacial surgical interventions. We propose a machine learning-based method to create a scale of facial deformity by producing numerical scores that reflect the level of deformity. An object detector that is constructed using a cascade function of Haar features has been trained with a rich dataset of normal faces in addition to a collection of images that does not contain faces. After that, the confidence score of the face detector was used as a gauge of facial abnormality. The scores were compared with a benchmark that is based on human appraisals obtained using a survey of a range of facial deformities. Interestingly, the overall Pearson's correlation coefficient of the machine scores with respect to the average human score exceeded 0.96.

published proceedings

  • Annu Int Conf IEEE Eng Med Biol Soc

author list (cited authors)

  • Takiddin, A., Shaqfeh, M., Boyaci, O., Serpedin, E., & Stotland, M.

citation count

  • 1

complete list of authors

  • Takiddin, Abdulrahman||Shaqfeh, Mohammad||Boyaci, Osman||Serpedin, Erchin||Stotland, Mitchell

publication date

  • July 2022