An Efficient and Lightweight Method for Human Ear Recognition Based on MobileNet

Abstract

In recent years, biometric recognition patterns have attracted the attention of many researchers, among which human ears, as a unique and stable biometric feature, have significant advantages in verifying personal identity. In the Internet era, a system with low computing cost and good real-time performance is more popular. Most of the existing ear recognition methods are based on a large parameter network model, which causes a large memory footprint and computational overhead. This paper proposes an efficient and lightweight human ear recognition method (ELERNet) based on MobileNet V2. Based on the MobileNet V2 model, dynamic convolution decomposition is introduced to enhance the representation ability of human ear features. Then, combined with the coordinate attention mechanism, the spatial features of human ear images are aggregated to locate the location information of the human ear features more accurately. We conducted experiments on AWE and EarVN1.0 human ear datasets. Compared with the MobileNet V2 model, the recognition accuracy of our method is significantly improved. Using less computing hardware resources, the ELERNet model achieves 83.52% and 96.10% Rank-1 (R1) recognition accuracy, respectively, which is better than other models. Finally, we provide a visual interpretation using GradCAM technology, and the results show that our method can learn specific and discriminative features in the ear images.