USTB-Helloear: A Large Database of Ear Images Photographed Under Uncontrolled Conditions

The capabilities of biometric systems, such as face or fingerprint recognition systems, have recently made extraordinary leaps by the emergence of deep learning. However, due to the lack of enough training data, the applications of deep neural network in the ear recognition filed have run into the bottleneck. Therefore, the motivation of this paper is to present a new large database that contains more than 610,000 profile images from 1570 subjects. The main distinguishing feature of the images in this USTB-Helloear database is that they were taken under uncontrolled conditions with illumination and pose variation. In addition, all of individuals were required to not particularly care about ear occlusions. Therefore, 30% of subjects had the additional control groups with different level of ear occlusions. The ear images can be utilized to train a deep learning model of ear detection and recognition; moreover, the database, along with pair-matching tests, provides a benchmark to evaluate the performances of ear recognition and verification systems.

[1]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.

[2]  Xiaogang Wang,et al.  Deep Learning Face Representation by Joint Identification-Verification , 2014, NIPS.

[3]  Yoshua Bengio,et al.  How transferable are features in deep neural networks? , 2014, NIPS.

[4]  Sergey Ioffe,et al.  Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning , 2016, AAAI.

[5]  Jian Sun,et al.  Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[6]  Ross B. Girshick,et al.  Fast R-CNN , 2015, 1504.08083.

[7]  Phalguni Gupta,et al.  SIFT-based ear recognition by fusion of detected keypoints from color similarity slice regions , 2009, 2009 International Conference on Advances in Computational Tools for Engineering Applications.

[8]  Xiaogang Wang,et al.  DeepID3: Face Recognition with Very Deep Neural Networks , 2015, ArXiv.

[9]  Zhichun Mu,et al.  Ear recognition based on local information fusion , 2012, Pattern Recognit. Lett..

[10]  Dumitru Erhan,et al.  Going deeper with convolutions , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[11]  Mark S. Nixon,et al.  Robust log-Gabor filter for ear biometrics , 2008, 2008 19th International Conference on Pattern Recognition.

[12]  Dariusz Frejlichowski,et al.  The West Pomeranian University of Technology Ear Database - A Tool for Testing Biometric Algorithms , 2010, ICIAR.

[13]  Zhengguang Xu,et al.  Using Ear Biometrics for Personal Recognition , 2005, IWBRS.

[14]  Ming Yang,et al.  DeepFace: Closing the Gap to Human-Level Performance in Face Verification , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[15]  Sergey Ioffe,et al.  Rethinking the Inception Architecture for Computer Vision , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[16]  Hui Zeng,et al.  Robust classification for occluded ear via Gabor scale feature-based non-negative sparse representation , 2013 .

[17]  Xiaogang Wang,et al.  Deep Learning Face Representation from Predicting 10,000 Classes , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[18]  Mark S. Nixon,et al.  Force field energy functionals for image feature extraction , 2002, Image Vis. Comput..

[19]  Phalguni Gupta,et al.  An efficient ear recognition technique invariant to illumination and pose , 2013, Telecommun. Syst..

[20]  Yi Zhang,et al.  Ear Detection under Uncontrolled Conditions with Multiple Scale Faster Region-Based Convolutional Neural Networks , 2017, Symmetry.

[21]  Arun Ross,et al.  Handbook of Biometrics , 2007 .

[22]  Xiaogang Wang,et al.  Deeply learned face representations are sparse, selective, and robust , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[23]  Marwan Mattar,et al.  Labeled Faces in the Wild: A Database forStudying Face Recognition in Unconstrained Environments , 2008 .

[24]  Zhenan Sun,et al.  A Lightened CNN for Deep Face Representation , 2015, ArXiv.

[25]  Kaiming He,et al.  Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks , 2015, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[26]  Long Chen,et al.  Partial Data Ear Recognition From One Sample per Person , 2016, IEEE Transactions on Human-Machine Systems.

[27]  Andrew Zisserman,et al.  Very Deep Convolutional Networks for Large-Scale Image Recognition , 2014, ICLR.

[28]  Andrea F. Abate,et al.  Ear Recognition by means of a Rotation Invariant Descriptor , 2006, 18th International Conference on Pattern Recognition (ICPR'06).

[29]  James Philbin,et al.  FaceNet: A unified embedding for face recognition and clustering , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[30]  Trevor Darrell,et al.  Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation , 2013, 2014 IEEE Conference on Computer Vision and Pattern Recognition.