EpNet: A deep neural network for ear detection in 3D point clouds
Advanced Concepts for Intelligent Vision Systems
School of Science
The human ear is full of distinctive features, and its rigidness to facial expressions and ageing has made it attractive to biometric research communities. Accurate and robust ear detection is one of the essential steps towards biometric systems, substantially affecting the efficiency of the entire identification system. Existing ear detection methods are prone to failure in the presence of typical day-to-day circumstances, such as partial occlusions due to hair or accessories, pose variations, and different lighting conditions. Recently, some researchers have proposed different state-of-the-art deep neural network architectures for ear detection in two-dimensional (2D) images. However, the ear detection directly from three-dimensional (3D) point clouds using deep neural networks is still an unexplored problem. In this work, we propose a deep neural network architecture named EpNet for 3D ear detection, which can detect ear directly from 3D point clouds. We also propose an automatic pipeline to annotate ears in the profile face images of UND J2 public data set. The experimental results on the public data show that our proposed method can be an effective solution for 3D ear detection.