A Hybrid Approach to Multimodal Biometric Recognition Based on Feature-level Fusion of Face, Two Irises, and Both Thumbprints

BACKGROUND: The most significant motivations for designing multi-biometric systems are high-accuracy recognition, high-security assurances as well as overcoming the limitations like non-universality, noisy sensor data, and large intra-user variations. Therefore, choosing data for fusion is of high significance for the design of a multimodal biometric system. The feature vectors contain richer information than the scores, decisions and even raw data, thereby making feature-level fusion more effective than other levels. METHOD: In the proposed method, kernel is used for fusion in feature space. First, the face features are extracted using kernel-based methods, the features of both right and left irises are extracted using Hough Transform and Daugman algorithm methods, and the features of both thumb prints are extracted using the Gabor filter bank. Second, after normalization operations, we use kernel methods to map the feature vectors to a kernel Hilbert space where non-linear relations are shown as linear for the purpose of compatibility of feature spaces. Then, dimensionality reduction algorithms are used to the fusion of the feature vectors extracted from fingerprints, irises and the face. since the proposed system uses face, both right 7and left irises and right and left thumbprints, it is hybrid multi-biometric system. We c8arried out the tests on seven databases. RESULTS: Our results show that the hybrid multimodal template, while being secure against spoof attacks and making the system robust, can use the dimensionality of only 15 features to increase the accuracy of a hybrid multimodal biometric system to 100%, which shows a significant improvement compared with uni-biometric and other multimodal systems. CONCLUSION: The proposed method can be used to search large databases. Consequently, a large database of a secure multimodal template could be correctly differentiated based on the corresponding class of a test sample without any consistency error.