Effective Classifier Identification in Biometric Pattern Recognition

Next-generation identity verification using biometric features is nearly foolproof with the right classifier. However, selecting the correct classifier poses a key challenge, particularly in the recognition of biometric patterns. High-potential projects may face delays due to a lack of the right recognition mechanism or the malfunction of the selected classifier. This could also result from not choosing the appropriate classifier that aligns with the project's patterns. This study aims to evaluate various classifiers with potential in biometric research and the capabilities of different machine learning algorithms. Several classifiers were experimentally evaluated in combination with dynamic algorithms. The ultimate objective was to identify a standard classifier suitable for general biometric pattern recognition. Using well-known biometric pattern datasets, multivariate algorithms, such as Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA), were applied. These methods were combined with different classifiers, including SVM-L, MLP, KNN, etc. After analyzing the results obtained, the combination of LDA with MLP outperformed other approaches in terms of accuracy.