论文标题
朝着强大而可扩展的指纹方向估计迈进：从梯度到深度学习
Advancing Toward Robust and Scalable Fingerprint Orientation Estimation: From Gradients to Deep Learning
论文作者
论文摘要
该研究确定了从传统方法到更先进的机器学习方法的明显发展。当前的算法面临着持续的挑战，包括降低图像质量，受损的山脊结构和背景噪声，这会影响性能。为了克服这些局限性，未来的研究必须集中于开发具有较低计算复杂性的有效算法，同时在各种条件下保持稳健的性能。将基于梯度的技术与机器学习的适应性和鲁棒性结合起来的简单性和效率的混合方法对于推进指纹识别系统特别有希望。指纹方向估计在提高生物识别系统的可靠性和准确性方面起着至关重要的作用。这项研究强调了当前方法的局限性，并强调了设计下一代算法的重要性，这些算法可以在不同的应用领域之间有效运行。通过解决这些挑战，未来的发展可以增强生物识别系统的可扩展性，可靠性和适用性，为在安全和识别技术中更广泛使用铺平道路。
The study identifies a clear evolution from traditional methods to more advanced machine learning approaches. Current algorithms face persistent challenges, including degraded image quality, damaged ridge structures, and background noise, which impact performance. To overcome these limitations, future research must focus on developing efficient algorithms with lower computational complexity while maintaining robust performance across varied conditions. Hybrid methods that combine the simplicity and efficiency of gradient-based techniques with the adaptability and robustness of machine learning are particularly promising for advancing fingerprint recognition systems. Fingerprint orientation estimation plays a crucial role in improving the reliability and accuracy of biometric systems. This study highlights the limitations of current approaches and underscores the importance of designing next-generation algorithms that can operate efficiently across diverse application domains. By addressing these challenges, future developments could enhance the scalability, reliability, and applicability of biometric systems, paving the way for broader use in security and identification technologies.
