The concept of biometric identification is centred around the theory that every individual is unique and has distinct characteristics. Various metrics such as fingerprint, face, iris, or retina are adopted for this purpose. Nonetheless, new alternatives are needed to establish the identity of individuals on occasions where the above techniques are unavailable. One emerging method of human recognition is lip-based identification. It can be treated as a new kind of biometric measure. The patterns found on the human lip are permanent unless subjected to alternations or trauma. Therefore, lip prints can serve the purpose of confirming an individual's identity. The main objective of this work is to design experiments using computer vision methods that can recognise an individual solely based on their lip prints. This article compares traditional and deep learning computer vision methods and how they perform on a common dataset for lip-based identification. The first pipeline is a traditional method with Speeded Up Robust Features with either an SVM or K-NN machine learning classifier, which achieved an accuracy of 95.45% and 94.31%, respectively. A second pipeline compares the performance of the VGG16 and VGG19 deep learning architectures. This approach obtained an accuracy of 91.53% and 93.22%, respectively.

中文翻译：

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使用传统和深度学习的基于唇纹的识别

生物特征识别的概念围绕着每个人都是独一无二的并且具有鲜明特征的理论。为此目的采用了各种指标，例如指纹、面部、虹膜或视网膜。尽管如此，在上述技术不可用的情况下，需要新的替代方法来确定个人的身份。一种新兴的人类识别方法是基于嘴唇的识别。它可以被视为一种新的生物测量方法。在人类嘴唇上发现的图案是永久性的，除非受到交替或外伤。因此，唇印可以起到确认个人身份的作用。这项工作的主要目标是使用计算机视觉方法设计实验，这些方法可以仅根据唇印识别个人。本文比较了传统和深度学习计算机视觉方法，以及它们如何在基于唇部识别的通用数据集上执行。第一个管道是一种传统方法，具有 Speed Up Robust Features 和 SVM 或 K-NN 机器学习分类器，其准确率分别达到 95.45% 和 94.31%。第二条管道比较了 VGG16 和 VGG19 深度学习架构的性能。这种方法的准确率分别为 91.53% 和 93.22%。第二条管道比较了 VGG16 和 VGG19 深度学习架构的性能。这种方法的准确率分别为 91.53% 和 93.22%。第二条管道比较了 VGG16 和 VGG19 深度学习架构的性能。这种方法的准确率分别为 91.53% 和 93.22%。