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.

An iris recognition system for identifying human identity using two feature extraction methods is proposed and implemented. The first approach is the Fourier descriptors, which is based on transforming the uniqueness iris texture to the frequency domain. The new frequency domain features could be represented in iris-signature graph. The low spectrums define the general description of iris pattern while the fine detail of iris is represented as high spectrum coefficients. The principle component analysis is used here to reduce the feature dimensionality as a second feature extraction and comparative method. The biometric system performance is evaluated by comparing the recognition results for fifty persons using the two methods. Three classifiers have been considered to evaluate the system performance for each approach separately. The classification results for Fourier descriptors on three classifiers satisfied 86% 94%, and 96%, versus 80%, 92%, and 94% for principle component analysis when Cosine, Euclidean, and Manhattan classifiers were applied respectively. These results approve that Fourier descriptors method as feature extractor has better accuracy rate than principle component analysis.