A novel fuzzy multimodal information fusion technology for human biometric traits identification

In recent years, biometric based security systems achieved more attention due to continuous terrorism threats around the world. However, a security system comprised of a single form of biometric information cannot fulfill users' expectations and may suffer from noisy sensor data, intra and inter class variations and continuous spoof attacks. To overcome some of these problems, multimodal biometric systems with multiple physiological, behavioral, and soft biometric information are becoming more popular due to increased recognition accuracy. In order to take full advantage of the multimodal approaches, one of the main issues is to implement the fusion mechanism for different biometric information. In this research, we utilize the physiological attributes (face, ear and iris) along with soft biometric information (gender, ethnicity and eye color). A fuzzy fusion mechanism for robust and reliable multimodal biometric based security systems is developed. The proposed fuzzy fusion scheme adopts rank, match score and soft biometrics information as the input and produces final identification decision via a fuzzy rule-based inference system. The experimental results show that the fuzzy fusion method can provide us faster, higher and more reliable recognition performance than conventional unimodal methods. The system can be effectively used at any security critical applications.