With the improvement of informatization and the development of computer technology, identity recognition with fingerprint has been the growing trend, but the stained fingerprint will make recognition difficult. To solve the above problem, this paper briefly introduced the traditional point matching fingerprint recognition algorithm and the damaged fingerprint recognition method based on the convolution neural network (CNN). Then in MATLAB software, the damaged fingerprint recognition method based on CNN was simulated and compared with the traditional point matching recognition method and traditional CNN recognition method. The results showed that the improved CNN recognition method iterated fewer times and achieved smaller training error during the training; the improved CNN method had lower false recognition rate and rejection rate in identifying the unknown fingerprint; the traditional point matching method spent the most time in identifying the unknown fingerprint, followed by the traditional CNN method and the improved CNN recognition method. This study used CNN for fingerprint recognition and improved CNN to improve its recognition accuracy for the damaged fingerprint. The improved CNN can effectively enhance the recognition accuracy of the damaged fingerprint, which provides a useful reference for the improvement of the accuracy and applicability of the fingerprint recognition system.

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受损指纹的特征提取，识别和匹配：深度学习网络的应用

随着信息化的改善和计算机技术的发展，带有指纹的身份识别已经成为一种增长趋势，但是沾污的指纹将使识别变得困难。为解决上述问题，本文简要介绍了传统的点匹配指纹识别算法和基于卷积神经网络的损伤指纹识别方法。然后在MATLAB软件中对基于CNN的指纹识别方法进行了仿真，并与传统的点匹配识别法和传统的CNN识别方法进行了比较。结果表明，改进的CNN识别方法在训练过程中迭代次数减少，训练误差较小。改进的CNN方法在识别未知指纹方面具有较低的错误识别率和拒绝率。传统的点匹配方法花费最多的时间来识别未知指纹，其次是传统的CNN方法和改进的CNN识别方法。这项研究使用CNN进行指纹识别，并改进了CNN以提高其对受损指纹的识别精度。改进的CNN可以有效提高受损指纹的识别精度，为提高指纹识别系统的准确性和适用性提供有益的参考。这项研究使用CNN进行指纹识别，并改进了CNN以提高其对受损指纹的识别精度。改进的CNN可以有效提高受损指纹的识别精度，为提高指纹识别系统的准确性和适用性提供有益的参考。这项研究使用CNN进行指纹识别，并改进了CNN以提高其对受损指纹的识别精度。改进的CNN可以有效提高受损指纹的识别精度，为提高指纹识别系统的准确性和适用性提供有益的参考。