Deep Hybrid Multimodal Biometric Recognition System Based on Features-Level Deep Fusion of Five Biometric Traits.

The need for information security and the adoption of the relevant regulations is becoming an overwhelming demand worldwide. As an efficient solution, hybrid multimodal biometric systems utilize fusion to combine multiple biometric traits and sources with improving recognition accuracy, higher security assurance, and to cope with the limitations of the uni-biometric system. In this paper, three strategies for dealing with a feature-level deep fusion of five biometric traits (face, both irises, and two fingerprints) derived from three sources of evidence are proposed and compared.

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.