Automatic Ear Detection and Segmentation over Partially Occluded Profile Face Images

  • Celia Cintas Instituto Patagónico de Ciencias Sociales y Humanas, Centro Nacional Patagónico, CONICET.
  • Claudio Delrieux Departamento de Ingeniería Eléctrica y Computadoras, Universidad Nacional del Sur - CONICET
  • Pablo Navarro Instituto Patagónico de Ciencias Sociales y Humanas, Centro Nacional Patagónico, CONICET.
  • Mirsha Quinto-Sánchez Ciencia Forense, Facultad de Medicina, Universidad Nacional Autónoma de México.
  • Bruno Pazos Instituto Patagónico de Ciencias Sociales y Humanas, Centro Nacional Patagónico, CONICET.
  • Rolando Gonzalez-José Instituto Patagónico de Ciencias Sociales y Humanas, Centro Nacional Patagónico, CONICET.
Keywords: biometrics, convex hull, deep learning, ear detection, occlusion

Abstract

Automated, non invasive ear detection in images and video is becoming increasingly required in several contexts, including nonivasive biometric identification, biomedical analysis, forensics, and many others. In biometric recognition systems, fast and robust ear detection is a crucial step within the recognition pipeline. Existing approaches to ear detection are susceptible to fail in the presence of typical everyday situations that prevent a crisp imaging of the ears, like partial occlusions, ear accessories, or uncontrolled camera and illumination conditions. Even more, most of the proposed solutions work efficiently only within a previously detected rectangular region of interest, which limits their applicability and lowers the accuracy of the overall detection. In this paper we evaluate the use of Convolutional Neural Networks (CNNs) together with Geometric Morphometrics (GM) for automatic ear detection in the presence of partial occlusions, and a Convex Hull algorithm for the ear area segmentation. A CNN was trained with a set of ear images landmarked by experts using GM to achieve high consistency. After training, the CNN is able to detect ears over profile faces, even in the presence of partial occlusions. We analyze the performance of the proposed ear detection and segmentation method over partially occluded ear images using the CVL Dataset

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Published
2019-04-17
How to Cite
Cintas, C., Delrieux, C., Navarro, P., Quinto-Sánchez, M., Pazos, B., & Gonzalez-José, R. (2019). Automatic Ear Detection and Segmentation over Partially Occluded Profile Face Images. Journal of Computer Science and Technology, 19(01), e08. https://doi.org/10.24215/16666038.19.e08
Section
Original Articles