User identification is becoming more and more important for Apps on mobile devices. However, the identity recognition based on eyes, e.g., iris recognition, is rarely used on mobile devices comparing with those based on face and fingerprint due to its extra cost in hardware and complicated operations during recognition. In this article, an eye-based recognition method is designed for identity recognition on mobile devices, which can be implemented just like face recognition. In the proposed method, the eye feature is composed of the static and dynamic features, where the periocular feature extracted by deep neural network from the eye image is used as the static feature, and the motion feature of saccadic velocity is selected as the dynamic feature. The eye images can be captured by the normal camera on mobile devices just like faces, and dynamic features can provide living information to increase the difficulty of forgery. The GazeCapture dataset is used to test the proposed method, because the eye images in this dataset are captured by mobile devices during daily use. The recognition accuracy of the proposed method on the GazeCapture dataset can reach 96.87% only based on the periocular feature and can be enhanced to 97.99% when it is fused with the saccadic feature. The experiment results show that the performance of the proposed method can be comparative to that of iris recognition methods. It demonstrates that the proposed method is a practical reference for the eye-based identity recognition, and the proposed method provides one more biometric choice for mobile devices.

中文翻译：

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基于眼睛的移动设备用户识别识别

用户识别对于移动设备上的应用程序变得越来越重要。然而，基于眼睛的身份识别，例如虹膜识别，与基于人脸和指纹的身份识别相比，由于硬件成本高且识别过程复杂，因此在移动设备上应用较少。本文设计了一种基于眼睛的识别方法，用于移动设备上的身份识别，可以像人脸识别一样实现。该方法将眼睛特征由静态特征和动态特征组成，将深度神经网络从人眼图像中提取的眼周特征作为静态特征，选择扫视速度的运动特征作为动态特征。 . 眼睛图像可以像人脸一样被移动设备上的普通相机捕获，动态特征可以提供活体信息，增加伪造的难度。GazeCapture 数据集用于测试所提出的方法，因为该数据集中的眼睛图像是在日常使用中由移动设备捕获的。该方法在 GazeCapture 数据集上的识别准确率仅基于眼周特征就可以达到 96.87%，在与眼周特征融合时可以提高到 97.99%。实验结果表明，该方法的性能可以与虹膜识别方法相媲美。表明该方法是基于眼睛的身份识别的实用参考，该方法为移动设备提供了更多的生物特征选择。GazeCapture 数据集用于测试所提出的方法，因为该数据集中的眼睛图像是在日常使用中由移动设备捕获的。该方法在 GazeCapture 数据集上的识别准确率仅基于眼周特征就可以达到 96.87%，在与眼周特征融合时可以提高到 97.99%。实验结果表明，该方法的性能可以与虹膜识别方法相媲美。表明该方法是基于眼睛的身份识别的实用参考，该方法为移动设备提供了更多的生物特征选择。GazeCapture 数据集用于测试所提出的方法，因为该数据集中的眼睛图像是在日常使用中由移动设备捕获的。该方法在 GazeCapture 数据集上的识别准确率仅基于眼周特征就可以达到 96.87%，在与眼周特征融合时可以提高到 97.99%。实验结果表明，该方法的性能可以与虹膜识别方法相媲美。表明该方法是基于眼睛的身份识别的实用参考，该方法为移动设备提供了更多的生物特征选择。该方法在 GazeCapture 数据集上的识别准确率仅基于眼周特征就可以达到 96.87%，在与眼周特征融合时可以提高到 97.99%。实验结果表明，该方法的性能可以与虹膜识别方法相媲美。表明该方法是基于眼睛的身份识别的实用参考，该方法为移动设备提供了更多的生物特征选择。该方法在 GazeCapture 数据集上的识别准确率仅基于眼周特征即可达到 96.87%，与眼周特征融合后可提高至 97.99%。实验结果表明，该方法的性能可以与虹膜识别方法相媲美。证明该方法是基于眼睛的身份识别的实用参考，该方法为移动设备提供了更多的生物特征选择。