Abstract
The ability of biometric systems has recently been dramatically improved by the emergency of deep learning. In the process of ear verification, the accuracy is often lower than expected because of the influence of pose variation and occlusion. In this paper, we propose a novel ear alignment approach. According to the morphological characteristics and the geometric characteristics of the ear, we define six key points on the ear, three located in the inner ear region, and three located on the outer contour of the ear. In order to detect these key points on the ear image automatically, we train a cascaded convolutional neural network using our newly released USTB-Helloear database. Then the alignment of the test ear image is accomplished by radiological transformation which will minimize the mean square error of the six key points between the test image and the template image. Experimental results show that using ear alignment, the accuracy of the ear verification system can be improved.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Nixon, M.S., Bouchrika, I., Arbab-Zavar, B., et al.: On use of biometrics in forensics: gait and ear. In: 2010 European Signal Processing Conference, pp. 1655–1659. IEEE (2010)
Huang, H., Liu, J., Feng, H., et al.: Ear recognition based on uncorrelated local Fisher discriminant analysis. Neurocomputing 74(17), 3103–3113 (2011)
Lakshmanan, L.: Efficient person authentication based on multi-level fusion of ear scores. IET Biometrics 2(3), 97–106 (2013)
Mu, Z.: Robust classification for occluded ear via Gabor scale feature-based non-negative sparse representation. Opt. Eng. 53(6), 061702 (2014)
Taigman, Y., Yang, M., Ranzato, M., et al.: DeepFace: closing the gap to human-level performance in face verification. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 1701–1708. IEEE Computer Society (2014)
Sun, Y., Wang, X., Tang, X.: Deep learning face representation from predicting 10,000 classes. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 1891–1898. IEEE Computer Society (2014)
Sun, Y., Wang, X., Tang, X.: Deep learning face representation by joint identification-verification, vol. 27, pp. 1988–1996 (2014)
Sun, Y., Wang, X., Tang, X.: Deeply learned face representations are sparse, selective, and robust. In: Computer Vision and Pattern Recognition, pp. 2892–2900. IEEE (2015)
Emeršič, Ž., Štruc, V., Peer, P.: Ear recognition: more than a survey. Neurocomputing 255, 26–39 (2017)
Yuan, L., Mu, Z., Liu, L.: Ear recognition based on kernel principal component analysis and support vector machine. J. Eng. Sci. 28(9), 890–895 (2006)
Gao, S., Mu, Z., Yuan, L., et al.: Normalization of ear image based on active shape model. In: National Conference on Image and Graphics, p. 4 (2006)
Tian, Y., Fan, W.: Ear recognition based on fusion of scale invariant feature transform and geometric feature. J. Opt. 28(8), 1485–1491 (2008)
Tian, L., Mu, Z.: Ear recognition based on deep convolutional network. In: International Congress on Image and Signal Processing, Biomedical Engineering and Informatics, pp. 437–441. IEEE (2017)
Zhang, Y., Mu, Z., Yuan, L., et al.: Ear verification under uncontrolled conditions with convolutional neural networks. IET Biometrics 7, 185–198 (2018)
Zhang, Y., Mu, Z., Yuan, L., Yu, C., Liu, Q.: USTB-Helloear: a large database of ear images photographed under uncontrolled conditions. In: Zhao, Y., Kong, X., Taubman, D. (eds.) ICIG 2017. LNCS, vol. 10667, pp. 405–416. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-71589-6_35
Qi, N., Li, L., Zhao, W.: Chinese adult auricular morphology measurement and classification. Tech. Acoust. 29(5), 518–522 (2010)
Dai, J., Li, Y., He, K., et al.: R-FCN: object detection via region-based fully convolutional networks (2016)
Acknowledgments
This article is supported by the National Natural Science Foundation of China (Grant No. 61300075).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Switzerland AG
About this paper
Cite this paper
Yuan, L., Zhao, H., Zhang, Y., Wu, Z. (2018). Ear Alignment Based on Convolutional Neural Network. In: Zhou, J., et al. Biometric Recognition. CCBR 2018. Lecture Notes in Computer Science(), vol 10996. Springer, Cham. https://doi.org/10.1007/978-3-319-97909-0_60
Download citation
DOI: https://doi.org/10.1007/978-3-319-97909-0_60
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-97908-3
Online ISBN: 978-3-319-97909-0
eBook Packages: Computer ScienceComputer Science (R0)