School of Mathematics, Statistics and Computer Science, University of KwaZulu-Natal, Durban, South Africa.
Comput Intell Neurosci. 2022 Aug 31;2022:3514807. doi: 10.1155/2022/3514807. eCollection 2022.
Biometrics is the recognition of a human using biometric characteristics for identification, which may be physiological or behavioral. The physiological biometric features are the face, ear, iris, fingerprint, and handprint; behavioral biometrics are signatures, voice, gait pattern, and keystrokes. Numerous systems have been developed to distinguish biometric traits used in multiple applications, such as forensic investigations and security systems. With the current worldwide pandemic, facial identification has failed due to users wearing masks; however, the human ear has proven more suitable as it is visible. Therefore, the main contribution is to present the results of a CNN developed using EfficientNet. This paper presents the performance achieved in this research and shows the efficiency of EfficientNet on ear recognition. The nine variants of EfficientNets were fine-tuned and implemented on multiple publicly available ear datasets. The experiments showed that EfficientNet variant B8 achieved the best accuracy of 98.45%.
生物识别技术是利用生物特征对人类进行识别和认证的一种技术，这些生物特征可以是生理特征，也可以是行为特征。生理生物特征包括人脸、耳朵、虹膜、指纹和手型；行为生物特征包括签名、声音、步态和击键模式等。已经开发了许多系统来区分用于多种应用的生物识别特征，例如法医调查和安全系统。随着当前全球大流行，由于用户戴口罩，面部识别已经失败；然而，人的耳朵被证明更加适合，因为它是可见的。因此，主要贡献是展示使用 EfficientNet 开发的 CNN 的结果。本文介绍了在这项研究中取得的成果，并展示了 EfficientNet 在耳朵识别方面的效率。微调了 EfficientNet 的九个变体，并在多个公开的耳朵数据集上进行了实现。实验表明，EfficientNet 变体 B8 达到了最佳的准确率 98.45%。
