基于遗传算法的神经网络指纹自动分类

Automatic Fingerprint Classification by GA-Based Neural Network

研究指纹的自动分类问题对解决大容量指纹库的匹配实时性有着重要的意义。笔者提出了一种新的指纹自动分类方法。该方法通过求取指纹方向图抽取了指纹的纹形特征 ,并将其送入神经网络进行分类识别 ,网络连接权系数采用遗传算法进行学习寻优 ,克服了单纯BP算法训练时间长、易陷入局部极值的缺点 ,同时提高了网络全局收敛的效率。测试结果表明 ,基于遗传算法的多层前向神经网络分类器对指纹图象的分类有良好的性能。

Fingerprint classification can provide an important indexing mechanism in a fingerprint database. An accurate and consistent classification can greatly reduce fingerprint matching time for large database. In the paper, by combining genetic algorithm and neural network is presented a fingerprint classification algorithm which is able to achieve an accurate classification. By inputting the global feature represented by directional image to three layer neural network trained by genetic algorithm, the fingerprints were classified into six categories: whorl, right loop, left loop, arch, double loop and undiscerning type successfully.