Abstract
Ear detection is an important step in ear recognition approaches. Most existing ear detection techniques are based on manually designing features or shallow learning algorithms. However, researchers found that the pose variation, occlusion, and imaging conditions provide a great challenge to the traditional ear detection methods under uncontrolled conditions. This paper proposes an efficient technique involving Multiple Scale Faster Region-based Convolutional Neural Networks (Faster R-CNN) to detect ears from 2D profile images in natural images automatically. Firstly, three regions of different scales are detected to infer the information about the ear location context within the image. Then an ear region filtering approach is proposed to extract the correct ear region and eliminate the false positives automatically. In an experiment with a test set of 200 web images (with variable photographic conditions), 98% of ears were accurately detected. Experiments were likewise conducted on the Collection J2 of University of Notre Dame Biometrics Database (UND-J2) and University of Beira Interior Ear dataset (UBEAR), which contain large occlusion, scale, and pose variations. Detection rates of 100% and 98.22%, respectively, demonstrate the effectiveness of the proposed approach.
Highlights
Ear based human recognition technology is a novel research field in biometric identification.Compared with classical biometric identifiers such as fingerprint, face, and iris, the ear has its distinctive advantages
We proposed an ear detection system based on a Multiple Scale Faster R-convolution neural network (CNN) deep learning model to detect human ears in 2D images, which were photographed under uncontrolled conditions
An efficient and fully automatic 2D ear detection system utilizing Multiple Scale Faster R-CNN is proposed in this paper to detect ears under uncontrolled conditions
Summary
Ear based human recognition technology is a novel research field in biometric identification. There are plenty of research papers and application products based on face detection, and identification in the wild has been reported in recent years. Numbers of vision tasks such as image classification [5,6], face recognition [7], and object detection [8] have obtained significant improved performance via deep learning models. We proposed an ear detection system based on a Multiple Scale Faster R-CNN deep learning model to detect human ears in 2D images, which were photographed under uncontrolled conditions. We have trained a Faster R-CNN model to detect ears in natural images. We remove the threshold value part from the last step of the Faster R-CNN approach and connect an ear region filtering module to it.
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