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.
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This article is supported by the National Natural Science Foundation of China (Grant No. 61300075).
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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
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DOI: https://doi.org/10.1007/978-3-319-97909-0_60
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