The security problems of online transactions by smartphones reveal extreme demand for reliable identity authentication systems. With a lower risk of forgery, richer texture, and more comfortable acquisition mode, compared with face, fingerprint, and iris, palmprint is rarely adopted for identity authentication. In this paper, we develop an effective and full-function palmprint authentication system regarding the application on an Android smartphone, which bridges the algorithmic study and application of palmprint authentication. In more detail, an overall system framework is designed with complete functions, including palmprint acquisition, key points location, ROI segmentation, feature extraction, and feature coding. Basically, we develop a palmprint authentication system having user-friendly interfaces and good compatibility with the Android smartphone. Particularly, on the one hand, to guarantee the effectiveness and efficiency of the system, we exploit the practical Log-Gabor filter for feature extraction and discuss the impact of filtering direction, downsampling ratio, and discriminative feature coding to propose an improved algorithm. On the other hand, after exploring the hardware components of the smartphone and the technical development of the Android system, we provide an open technology to extend the biometric methods to real-world applications. On the public PolyU databases, simulation results suggest that the improved algorithm outperforms the original one with a promising accuracy of 100% and a good speed of 0.041 seconds. In real-world authentication, the developed system achieves an accuracy of 98.40% and a speed of 0.051 seconds. All the results verify the accuracy and timeliness of the developed system.

中文翻译：

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基于Android智能手机的掌纹认证系统的研究与开发

智能手机进行在线交易的安全性问题表明，人们对可靠的身份认证系统有极大的需求。与面部，指纹和虹膜相比，伪造的风险较低，纹理更丰富且获取方式更舒适，很少使用掌纹进行身份验证。在本文中，我们针对Android智能手机上的应用程序开发了一个有效的全功能掌纹认证系统，该体系架起了掌纹认证的算法研究和应用的桥梁。更详细地，设计具有完整功能的整体系统框架，包括掌纹采集，关键点定位，ROI分割，特征提取和特征编码。基本上，我们开发了一种掌上式身份验证系统，该系统具有用户友好的界面并与Android智能手机具有良好的兼容性。特别是，一方面，为了保证系统的有效性和效率，我们利用实用的Log-Gabor滤波器进行特征提取，并讨论了滤波方向，下采样率和区分特征编码的影响，提出了一种改进的算法。另一方面，在研究了智能手机的硬件组件和Android系统的技术开发之后，我们提供了一种开放技术，可将生物识别方法扩展到实际应用中。在公开的PolyU数据库上，仿真结果表明，改进的算法以100％的准确性和0.041秒的良好速度优于原始算法。在真实身份验证中，开发的系统可达到98.40％的精度和0.051秒的速度。所有结果都验证了所开发系统的准确性和及时性。