Balti Ala, Hamdi Abdelaziz, Abid Sabeur, Ben Khelifa Mohamed Moncef, Sayadi Mounir
Research Laboratory SIME, ENSIT, University of Tunis, Tunis, Tunisia.
J-AP2S Laboratory, South University, Toulon, France.
Front Artif Intell. 2024 Aug 5;7:1433494. doi: 10.3389/frai.2024.1433494. eCollection 2024.
This research introduces a novel MOMENTS-SVD vector for fingerprint identification, combining invariant moments and SVD (Singular Value Decomposition), enhanced by a modified PCA (Principal Component Analysis). Our method extracts unique fingerprint features using SVD and invariant moments, followed by classification with Euclidean distance and neural networks. The MOMENTS-SVD vector reduces computational complexity by outperforming current models. Using the Equal Error Rate (EER) and ROC curve, a comparative study across databases (CASIA V5, FVC 2002, 2004, 2006) assesses our method against ResNet, VGG19, Neuro Fuzzy, DCT Features, and Invariant Moments, proving enhanced accuracy and robustness.
本研究引入了一种用于指纹识别的新型MOMENTS-SVD向量，它结合了不变矩和奇异值分解（SVD），并通过改进的主成分分析（PCA）进行增强。我们的方法使用SVD和不变矩提取独特的指纹特征，然后通过欧几里得距离和神经网络进行分类。MOMENTS-SVD向量通过优于当前模型降低了计算复杂度。使用等错误率（EER）和ROC曲线，在多个数据库（CASIA V5、FVC 2002、2004、2006）上进行的对比研究将我们的方法与ResNet、VGG19、神经模糊、离散余弦变换特征和不变矩进行了评估，证明了其更高的准确性和鲁棒性。
