journal article·2023-05-30Open access

Lightweight YOLOv5s Human Ear Recognition Based on MobileNetV3 and Ghostnet

Yanmin Lei, Dong Pan, Zhibin Feng, Junru Qian

Applied Sciences· Volume 13· Issue 11

Abstract

Ear recognition is a biometric identification technology based on human ear feature information, which can not only detect the human ear in the picture but also determine whose human ear it is, so human identity can be verified by human ear recognition. In order to improve the real-time performance of the ear recognition algorithm and make it better for practical applications, a lightweight ear recognition method based on YOLOv5s is proposed. This method mainly includes the following steps: First, the MobileNetV3 lightweight network is used as the backbone network of the YOLOv5s ear recognition network. Second, using the idea of the Ghostnet network, the C3 module and Conv module in the YOLOv5s neck network are replaced by the C3Ghost module and GhostConv module, and then the YOLOv5s-MG ear recognition model is constructed. Third, three distinctive human ear datasets, CCU-DE, USTB, and EarVN1.0, are collected. Finally, the proposed lightweight ear recognition method is evaluated by four evaluation indexes: mAP value, model size, computational complexity (GFLOPs), and parameter quantity (params). Compared with the best results of YOLOv5s, YOLOv5s-V3, YOLOv5s-V2, and YOLOv5s-G methods on the CCU-DE, USTB, and EarVN1.0 three ear datasets, the params, GFLOPS, and model size of the proposed method YOLOv5s-MG are increased by 35.29%, 38.24%, and 35.57% respectively. The FPS of the proposed method, YOLOv5s-MG, is superior to the other four methods. The experimental results show that the proposed method has the performance of larger FPS, smaller model, fewer calculations, and fewer parameters under the condition of ensuring the accuracy of ear recognition, which can greatly improve the real-time performance and is feasible and effective.
Source: crossref · Quality: baseline

Research context

OpenAlex topics: Biometric Identification and Security · Face recognition and analysis · Reconstructive Facial Surgery Techniques

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

Affiliations

Department of Electrical and Information Engineering, Changchun University, Changchun 130022, China

Institute of Science and Technology, Changchun Humanities and Sciences College, Changchun 130028, China

Aviation Basic College, Air Force Aviation University, Changchun 130022, China

Jilin Province Key Laboratory of Measuring Instrument and Technology, Jilin Institute of Metrology, Changchun 130103, China

Funding

Science Technology Department of Jilin Province · Education Department of Jilin Province

References (41)

Show source-provided references
1. Jain, A.K., Pankanti, S., Prabhakar, S., Lin, H., and Ross, A. (2004, January 26). Biometrics: A grand challenge. Proceedings of the International Conference on Pattern Recognition, Cambridge, UK.10.1109/ICPR.2004.1334413 ↗
2. Wang, S.N. (2018). Research on Ear Recognition Based on Deep Learning. [Master’s Thesis, University of Science and Technology Liaoning].
3. Ding, Y.M. (2020). Research on Ear Recognition Based on Improved Sparse Representation. [Master’s Thesis, Harbin University of Science and Technology].
4. Summary of Face Recognition Technology
5. Sakthimohan, M., Rani, G.E., Navaneethkrihnan, M., Janani, K., Nithva, V., and Pranav, R. (2023, January 9–11). Detection and Recognition of Face Using Deep Learning. Proceedings of the 2023 International Conference on Intelligent Systems for Communication, IoT and Security (ICISCoIS), Coimbatore, India.
6. Ji, S.D. (2017). Research on the Fingerprint Identification Technology and Attendance System Application. [Master’s Thesis, Nanjing University of Posts and Telecommunications].
7. Overview of Fingerprint Identification Technology Development
8. Multi-feature recognition of palm vein and palm print in single near-infrared palm image
9. Chen, Y.H. (2015). Research and Implementation of Iris Recognition Key Problems. [Master’s Thesis, Jilin University].
10. Jiao, X.H. (2018). Research and Implementation of Iris Authentication Technology Based on Embedded. [Master’s Thesis, Heilongjiang University].
11. Zhang, Y. (2008). Ear Detection and Recognition under Uncontrolled Conditions Based on Deep Learning Algorithm. [Ph.D. Thesis, University of Science and Technology Beijing].
12. Ear authentication using log-Gabor wavelets. Biometric Technology for Human Identification IV
13. AsmaaSabet, A., Kareem Kamal A, G., and Hesham, E. (2015, January 23–25). Human Ear Recognition Using SIFT Features. Proceedings of the 2015 Third World Conference on Complex Systems (WCCS), Marrakech, Morocco.
14. Nosrati, M.S., Faez, K., and Faradji, F. (2007, January 25–28). Using 2D wavelet and principal component analysis for personal identification based on 2D ear structure. Proceedings of the 2007 International Conference on Intelligent and Advanced Systems, Kuala Lumpur, Malaysia.10.1109/ICIAS.2007.4658461 ↗
15. Omara, I., Li, X.M., Xiao, G., Adil, K., and Zuo, W. (2018, January 18–21). Discriminative local feature fusion for ear recognition problem. Proceedings of the 2018 8th International Conference on Bioscience, Biochemistry and Bioinformatics (ICBBB 2018), Association for Computing Machinery, New York, NY, USA.10.1145/3180382.3180409 ↗
16. Multi-pose ear recognition based on LLE
17. Fast 3D ear recognition based on local and global information
18. Susan, E. (2018, January 28–31). Ear Detection in the Wild using Faster R-CNN. Proceedings of the 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), Barcelona, Spain.
19. Lei, Y.M., Du, B.W., Qian, J.R., and Feng, Z.B. (2020, January 6–8). Research on Ear Recognition Based on SSD_MobileNet_v1 Network. Proceedings of the 2020 Chinese Automation Congress (CAC), Shanghai, China.10.1109/CAC51589.2020.9326541 ↗
20. Qian, J.R. (2020). Research on Dynamic Human Ear Recognition Method Based on Deep Learning. [Master’s Thesis, Chang Chun University].
21. Qi, J. (2020). Research on Target Identification Method Based on Human Ear Detection Technology. [Master’s Thesis, ChangChun University].
22. Redmon, J., Divvala, S., Girshick, R., and Farhadi, A. (2016, January 27–30). You only look once: Unified real-time object detection. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.10.1109/CVPR.2016.91 ↗
23. Redmon, J., and Farhadi, A. (2017, January 21–26). YOLO9000: Better, Faster, Stronger. Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.10.1109/CVPR.2017.690 ↗
24. Redmon, J., and Farhadi, A. (2018). YOLOv3: An Incremental Improvement. arXiv.
25. Bochkovskiy, A., Wang, C.Y., and Liao, H.Y.M. (2020). YOLOv4: Optimal Speed and Accuracy of Object Detection. arXiv.
26. Jirarat, I., Surapon, N.C., and Suchart, Y. (2021, January 10–12). Deep Learning-based Face Mask Detection Using YoloV5. Proceedings of the 2021 9th International Electrical Engineering Congress, Pattaya, Thailand.
27. Li, C., Li, L., Jiang, H., Weng, K., Geng, Y., Li, L., Ke, Z., Li, Q., Cheng, M., and Nie, W. (2022). YOLOv6: A single-stage object detection framework for industrial applications. arXiv.
28. Wang, C.Y., Bochkovskiy, A., and Liao, H.Y.M. (2022). YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors. arXiv.
29. Ju, R.Y., and Cai, W.M. (2023). Fracture Detection in Pediatric Wrist Trauma X-ray Images Using YOLOv8 Algorithm, Springer.10.1038/s41598-023-47460-7 ↗
30. Howard, A., Sandler, M., Chu, G., Chen, L.C., Chen, B., Tan, M.X., Wang, W.J., Zhu, Y.K., Pang, R.M., and Vasudevan, V. (November, January 27). Searching for MobileNetV3. International Conference on Computer Vision. Proceedings of the 2019 IEEE/CVF International Conference on Computer Vision (ICCV), Seoul, Republic of Korea.10.1109/ICCV.2019.00140 ↗
31. Han, K., Wang, Y., Tian, Q., Guo, J., Xu, C., and Xu, C. (2020, January 13–19). GhostNet: More Features from Cheap Operations. Proceedings of the 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA.10.1109/CVPR42600.2020.00165 ↗
32. ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design
33. Howard, A.G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., and Adam, H. (2017). MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications. arXiv.
34. Sandler, M., Howard, A., and Zhu, M. (2018, January 18–23). MobileNetV2: Inverted Residuals and Linear Bottlenecks. Proceedings of the 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.10.1109/CVPR.2018.00474 ↗
35. Zhang, X., Zhou, X., and Lin, M. (2018, January 18–23). ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices. Proceedings of the 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.10.1109/CVPR.2018.00716 ↗
36. Lei, Y., Qian, J., Pan, D., and Xu, T. (2022). Research on Small Sample Dynamic Human Ear Recognition Based on Deep Learning. Sensors, 22.10.3390/s22051718 ↗
37. (2021, December 30). Available online: http://www1.ustb.edu.cn/resb/visit/visit.htm.
38. EarVN1.0: A new large-scale ear images dataset in the wild
39. Improved YOLOv5 lightweight mask detection algorithm
40. Chen, K., Liu, X., Ja, L.J., Fang, Y.L., and Zhao, C.X. (2023). Insulator Defect Detection Based on Lightweight Network and Enhanced Multi-scale Feature. High Volt. Eng., 1–14.
41. Zou, P., Yang, K.J., and Liang, C. (2023). Improved YOLOv5 algorithm for real-time detection of irregular driving behavior. Comput. Eng. Appl., 1–9. Available online: http://kns.cnki.net/kcms/detail/11.2127.TP.20230206.1311.003.html.