Multi-biometrics fusion based on dynamic weighting of characteristic signal quality

Abstract

Face recognition and speaker recognition were integrated at the decision-making level. In order to cope with the influence of the external environment on the recognition result, image quality and sound quality assessment methods were introduced. By evaluating the quality of information, features with poor information quality were removed. Information quality dynamically adjusted the weight proportion of the module, and rejected individuals with low matching degree of single-mode feature recognition. Then, according to DS theory, each evidence was merged into a new body of evidence to realize user identification. The experimental results show that the fusion method which takes into account the characteristic signal quality can effectively improve the recognition accuracy and security.

Keywords

• face recognition
• speaker recognition
• characterization of signal quality
• d-s evidence theory
• multi-biometrics fusion