A novel ear elements segmentation algorithm on depth map images.

The growing interest in the auricular anatomy is due to two different strands of research: 1) in the medical field it is associated with autologous ear reconstruction, a surgery adopted following trauma or congenital malformations; 2) in surveillance and law enforcement the ear is used for human detection and recognition. Alternative systems of ear analysis can be differentiated for the type of input data (two-dimensional, three-dimensional or both), for the type of acquisition tools (3D scanner, photographs, video surveillance, etc.) and finally for the adopted algorithms. Although the segmentation and recognition of the ear from the face is a widely discussed topic in literature, the detection and recognition of individual anatomical elements has not yet been studied in depth. To this end, this work lays the foundation for the identification of the auricular elements through image processing algorithms. The proposed algorithm automatically identifies the contours of the main anatomical elements by processing depth map images. The algorithm was tested qualitatively and quantitatively on a dataset composed of 150 ears. The qualitative evaluation was performed with the collaboration of medical staff and the quantitative tests were performed using manually annotated ground truth data.