Experimental Analysis of Face and Iris Biometric Traits Based on the Fusion Approach

Objectives : To develop an efficient algorithm for face and iris multimodal traits on ORL CASIA dataset to increase the performance rate decrease error of model. The main goal is Methods: proposed utilizes a fusion modalities using Stationary Wavelet Transform (SWT) Local Binary Pattern (LBP) techniques. Principal Component Analysis (PCA) applied reduce dimensionality each sample, improving efficiency while preserving most relevant information. characteristics from both are fused create comprehensive pattern individual. Findings: obtained features compared with database images Euclidean Distance classifier. model evaluated datasets. accuracy achieved by 99.42%, demonstrating robustness. Novelty: introduces feature-level fusion, combining modalities. encompasses training recognition phases within biometric system. During phase, modality captured processed SWT+LBP+PCA techniques form template user. These templates later stored in purposes. Keywords: Biometrics; Trait; Face; Iris; Multimodal;