Gauging Facial Abnormality Using Haar-Cascade Object Detector.

Journal: Annual International Conference Of The IEEE Engineering In Medicine And Biology Society. IEEE Engineering In Medicine And Biology Society. Annual International Conference

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.

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

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