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.
|Title of host publication||Advanced Concepts for Intelligent Vision Systems - 20th International Conference, ACIVS 2020, Proceedings|
|Editors||Jacques Blanc-Talon, Patrice Delmas, Wilfried Philips, Dan Popescu, Paul Scheunders|
|Place of Publication||Switzerland|
|Publisher||Springer Nature Switzerland AG|
|Number of pages||12|
|Publication status||Published - 2020|
|Event||20th International Conference on Advanced Concepts for Intelligent Vision Systems, ACIVS 2020 - Auckland, New Zealand|
Duration: 10 Feb 2020 → 14 Feb 2020
|Name||Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)|
|Conference||20th International Conference on Advanced Concepts for Intelligent Vision Systems, ACIVS 2020|
|Period||10/02/20 → 14/02/20|
Advanced Concepts for Intelligent Vision Systems
Feb. 10-14, 2020
Auckland, New Zealand
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The proceedings of Acivs 2020 (LNCS volume 12002) are available at the Springer on-line.website.
Acivs 2020 is a conference focusing on techniques for building adaptive, intelligent, safe and secure imaging systems. Acivs 2020 consists of five days of lecture sessions, both regular (25 minutes) and invited presentations, and poster sessions. The proceedings of Acivs 2020 are published by Springer in the Lecture Notes in Computer Science series and are listed in the ISI proceedings index.
Acivs 2020 features a conference dinner, and other social activities.