## 1 Introduction  
With the development of internet technology, biometric identification technologies have gradually developed. Biometric recognition is based on human features for identification. Generally speaking, the features that can be used for biometric identification should meet the basic attributes of universality, uniqueness, stability, and collectability [1]. When identifying different types of biometric features, their information retention, area, and anti-counterfeiting should also be considered [2]. At present, the commonly used biometric features are mainly face, ear, iris, palmprint, and fingerprint. As a branch of biometric recognition [3], the human ear has fewer features than the face and is not affected by facial expressions, age, makeup, and other factors [4, 5]. Compared with palmprint and fingerprint features, it is not easy to damage and lose information [6, 7, 8]. Compared with iris features, it provides rich structural information and texture features [9, 10]. Moreover, due to the unique recognition angle of the human ear, the requirements for image acquisition equipment are low, so it is widely studied by scholars. In recent years, with the rapid development of deep-learning-related technologies, the problem of ear recognition in complex environments has also been solved. Although the accuracy of human ear recognition is improved, the human ear recognition network is more complex, increasing the amount of computation, the number of parameters, and the size of the model. Therefore, on the premise of guaranteeing accuracy, reducing the computational burden of the human ear recognition network, the number of parameters, and model size, improvement in the algorithm of real-time ear recognition is of great importance to practical applications. At present, there are two main categories of ear recognition methods: traditional methods and methods based on deep learning [11]. The traditional method is to extract the global or local features of the human ear from the human ear image for ear recognition. In 2007, [PERSON] and [PERSON] [12] used log-Gabor wavelet technology to extract phase information from human ear grey images for ear recognition. In 2015, scholars, such as [PERSON] [13], extracted SIFT key points from human ear images, calculated the descriptors of each key point, and used the minimum distance classifier for human ear recognition. In 2007, [PERSON] [14] proposed a human ear recognition method based on a two-dimensional wavelet transform, using PCA to complete image classification. In 2018, [PERSON] et al. [15] proposed an improved unconstrained ear recognition method based on local feature fusion. LPQ, HOG, LBP, POEM, BSIF, and Gabor were used to extract the local features of human ear images, and then discriminant correlation analysis (DCA) was used for fusion dimension reduction. Finally, a support vector machine (SVM) was used for classification. In 2008, [PERSON]. [16] and other scholars proposed multi-pose ear recognition based on local linear embedding and introduced popular learning algorithms to improve the robustness of ear recognition. In 2019, [PERSON]. [17] and other scholars proposed a fast 3D ear recognition method based on local and global information for 3D ear recognition methods. The two-stage network is divided into two steps for ear recognition. The first step is to generate a candidate box for the ear target, and the second step is to determine the ear category. The representative network mainly has a Faster R-CNN network. In 2018, [PERSON] et al. [18] proposed an ear detection system based on the two-stage network Faster R-CNN and verified it on a test set composed of UDN, FERET, WVU, and AWE ear datasets, with a detection rate of 98%. [PERSON]. [11] proposed a multi-scale Faster-R-CNN ear detection algorithm to solve the problem of poor robustness of ear recognition in uncontrolled scenarios, and the accuracy of USTB-WebEar and USTB-Helloear has reached 99%.