A unimodal biometric system based Local Binary Pattern (LBP) and Gray Level Co-occurrence Matrix (GLCM) is developed to recognize the facial of 40 subjects. The matching process is implemented using three classifiers: Euclidean distance, Manhattan distance, and Cosine distance. The maximum accuracy (100%) is satisfied when GLCM and LBP are applied with Euclidean distance. The accuracy result of these two methods is advanced the Principle Component Analysis (PCA) and Fourier Descriptors (FDs) recognition rate. The ORL database is considered for constructing the proposed biometric system. 

This paper proposed feature level fusion technique to develop a robust multimodal human identification system. The humane face-iris traits are fused together to improve system accuracy in recognizing 40 persons taken from ORL and CASIA-V1 database. Also, low quality iris images of MMU-1 database are considered in this proposal for further test of recognition accuracy. The face-iris features are extracted using four comparative methods. The texture analysis methods like Gray Level Co-occurrence Matrix (GLCM) and Local Binary Pattern (LBP) are both gained 100% accuracy rate, while the Principle Component Analysis (PCA) and Fourier Descriptors (FDs) methods achieved 97.5% accuracy rate only.