# Lightweight Human Ear Recognition Based on Attention Mechanism and Feature Fusion
## 1 Introduction  
Due to the increasing computing power of computers, biometric identification technology has been widely developed and applied. Biometric recognition is based on human features for identification. At present, the commonly used biometric features are mainly face, ear, iris, palmprint, gait, voice and fingerprint. Because the human ear is stable, non-invasive, easy to collect, has low requirements for image acquisition equipment, andis not easily affected by factors such as glasses, emotions and makeup, it has attracted the attention of many researchers. In 2023, [PERSON] et al. used a search criterion of publications not later than 10 years and downloaded 1121 articles from 10 databases: Taylor & Francis, Springer, Science Direct, ACM, Emerald, Sage, Elsevier, Wiley, MIT and IEEE explore [1]. At present, ear recognition methods mainly include traditional methods and deep learning-based methods. The deep learning methods for ear recognition mainly include two-stage Faster R-CNN and single-stage SSD and YOLO series. In 2017, [PERSON] proposed an efficient and fully automatic 2D ear detection system utilizing multiple scale faster R-CNN on the UND-J2 database [2]. In 2017, [PERSON], [PERSON] et al. [3] proposed a face and ear detection method by using Faster R-CNN. In 2022, [PERSON] et al. [4] introduced a new database, NITJEW, and used the modified deep learning models Faster-RCNN and VGG-19 for ear detection and ear recognition tasks, respectively. In 2021, [PERSON] et al. [5] proposed the CED-Net model for ear recognition and compared it with the Faster-RCNN and SSD methods. [PERSON]. [6] used YOLOv3 for dynamic human ear recognition. In 2021, [PERSON]. [7] used YOLOv5 to locate multiple tiny ears with a short inference time. The authors of [4; 8; 9; 10; 11; 12; 13] also used different deep learning network models to achieve ear recognition. In addition to 2D ear recognition, the authors of [14; 15] also implemented 3D ear recognition by deep learning. In 2022, [PERSON] et al. [16] implemented gender recognition based on human ear images and deep learning. Through the study of the above references, it is found that the method based on deep learning can realize dynamic, static, 2D and 3D ear recognition with various pose changes. However, when CNN is used to solve the problem of ear recognition, there will be a large amount of calculation, parameter quantity and model size. The real-time performance of a single-stage network is better than that of a two-stage network. In order to further improve the real-time performance of single-stage networks, many scholars use lightweight networks to improve single-stage networks. Common lightweight networks include MobileNet series, Shufflenet series and Ghostnet. In 2023, [PERSON], [PERSON] et al. [17] proposed the YOLOv5s-MG method which has better real-time performance than YOLOv5s-V3, YOLOv5s-V2 and YOLOv5s-G. However, while improving real-time performance, it may also reduce the accuracy of human ear recognition on some human ear datasets. For a practical application of the ear recognition system, the ear recognition rate is the most important evaluation index. If the human ear recognition rate is low, there will be missed recognition or false recognition, which will affect the application of the system. Because the [EMAIL_ADDRESS] value of the YOLOv5s-MG method is 0.855, the goal of this paper is how to improve it to more than 0.9. on the EarVN1.0 ear dataset in this paper. The main contribution of this paper is to propose a lightweight ear recognition method based on the attention mechanism and feature fusion named YOLOv5s-MG-CBAM-F. In this method, the CBAM attention mechanism was added to the connection between the backbone network and the neck network of the lightweight human ear recognition method YOLOv5s-MG. Then, the SPPF layer and cross-regional feature fusion in the above method were added to further improve the accuracy of ear recognition.