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Title: Ear Detection in the Wild Using Faster R-CNN Deep Learning
Ear recognition has its advantages in identifying non-cooperative individuals in unconstrained environments. Ear detection is a major step within the ear recognition algorithmic process. While conventional approaches for ear detection have been used in the past, Faster Region-based Convolutional Neural Network (Faster R-CNN) based detection methods have recently achieved superior detection performance in various benchmark studies, including those on face detection. In this work, we propose an ear detection system that uses Faster R-CNN. The training of the system is performed on two stages: First, an AlexNet model is trained for classifying ear vs. non-ear segments. Second, the unified Region Proposal Network (RPN) with the AlexNet, that shares the convolutional features, are trained for ear detection. The proposed system operates in real-time and accomplishes 98 % detection rate on a test set, composed of data coming from different ear datasets. In addition, the system's ear detection performance is high even when the test images are coming from un-controlled settings with a wide variety of images in terms of image quality, illumination and ear occlusion.  more » « less
Award ID(s):
1650474
PAR ID:
10091249
Author(s) / Creator(s):
; ;
Date Published:
Journal Name:
IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)
Page Range / eLocation ID:
1124 to 1130
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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