This paper introduces an intelligent computational approach to automatically authenticate fingerprint for personal identification and verification. The feature vector is formed using combined features obtained from Gabor filtering technique and deep learning technique such as Convolutional Neural Network (CNN). Principle Component Analysis (PCA) has been performed on the feature vectors to reduce the overfitting problems in order to make the classification results more accurate and reliable. A multiclass classifier has been trained using the extracted features. Experiments performed using standard public databases demonstrated that the proposed approach showed better performance with regard to accuracy (99.87%) compared to the more recent classification techniques such as Support Vector Machine (97.86%) or Random Forest (95.47%). However, the proposed method also showed higher accuracy compared to other validation approaches such as K-fold (98.89%) and generalization (97.75%). Furthermore, these results were supported by confusion matrix results where only 10 failures were found when tested with 5000 images.

本文介绍了一种智能计算方法来自动验证指纹以进行个人识别和验证。特征向量是使用从 Gabor 滤波技术和深度学习技术如卷积神经网络 (CNN) 获得的组合特征形成的。对特征向量进行主成分分析（PCA），以减少过拟合问题，使分类结果更加准确可靠。已使用提取的特征训练多类分类器。使用标准公共数据库进行的实验表明，与支持向量机 (97.86%) 或随机森林 (95.47%) 等更新的分类技术相比，所提出的方法在准确性 (99.87%) 方面表现出更好的性能。然而，与 K 折（98.89%）和泛化（97.75%）等其他验证方法相比，所提出的方法还显示出更高的准确性。此外，这些结果得到了混淆矩阵结果的支持，当使用 5000 张图像进行测试时，仅发现 10 个失败。