A parameter to measure, how much predicted bounding boxes by the model are fit to ground truth bounding boxes.
Published in Chapter:
EarLocalizer: A Deep-Learning-Based Ear Localization Model for Side Face Images in the Wild
Aman Kamboj (National Institute of Technology, India), Rajneesh Rani (National Institute of Technology, India), and Aditya Nigam (Indian Institute of Technology, India)
Copyright: © 2019
|Pages: 23
DOI: 10.4018/978-1-5225-7525-2.ch006
Abstract
With much concern over security, it has become essential to maintain the identity and track of an individual's activities in the modern healthcare sector. Although there are biometric authentication systems based on different modalities, recognition of a person using the ear has gained much attention as ears are unique. Ear localization is a first step for ear-based biometric authentication systems, and this needs to be accurate, since it plays a crucial role in the overall performance of the system. The localization of ear in the side face images captured in the wild possess great challenges due to varying angles, light, scale, background clutter, blur and occlusion, etc. In this chapter, the authors have proposed EarLocalizer model to localize the ear, which is inspired by Faster-RCNN. The model is evaluated on two wild ear databases, UBEAR-II and USTB-III, and has achieved an accuracy of 95% and 99.08%, respectively, at IOU (Intersection over Union) = 0.5. The results of the proposed model signify that the model is invariant to the environmental conditions.