Lightweight YOLOv5s Human Ear Recognition Based on MobileNetV3 and Ghostnet

• Published in: Applied sciences Vol. 13; no. 11; p. 6667
• Main Authors: Lei, Yanmin, Pan, Dong, Feng, Zhibin, Qian, Junru
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 30.05.2023
• Subjects: Accuracy; Algorithms; Analysis; Biometric identification; Biometrics; Biometry; Classification; Datasets; Deep learning; Ear; ear recognition; Ghostnet; Lightweight; Mathematical models; Methods; MobileNetV3; Parameters; Performance indices; Real time; Target recognition; Telecommunication systems; Wavelet transforms; YOLOv5s; China
• ISSN: 2076-3417; 2076-3417
• DOI: 10.3390/app13116667

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.