Contents
What is rank level fusion?
The rank level fusion refers to the mechanism of combining such output ranks from the various biometrics matchers (subsystems), to consolidate the combined output ranks to establish the identity of an individual with higher confidence.
How is FRR calculated?
FAR = FPR = FP/(FP+TN) FRR is calculated as a fraction of positive scores falling below your threshold.
What type of error is false rejections?
False reject rate A false rejection occurs when an authorized subject is rejected by the biometric system as unauthorized. False rejections are also called a Type I error.
What is meant by false rejection rate?
False Acceptance Rate (FAR): the percentage of identification instances in which unauthorised persons are incorrectly accepted. False Rejection Rate (FRR): the percentage of identification instances in which authorised persons are incorrectly rejected.
What do you mean by false rejection rate?
False Rejection Rate (FRR): the percentage of identification instances in which authorised persons are incorrectly rejected.
How is score level fusion of multimodal biometrics?
A general approach is proposed for the fusion at score level by combining the scores from multiple biometrics using triangular norms (t-norms) due to Hamacher, Yager, Frank, Schweizer and Sklar, and Einstein product. This study aims at tapping the potential of t-norms for multimodal biometrics.
Can a fusion be used at score level?
The fusion at score level can be applied to all types of biometric systems ( Sudhamani et al., 2012 ). There are two approaches to fuse the scores obtained by different matchers.
Is the fusion at the decision level rigid?
Fusion at the decision level is considered to be the rigid due to the lack of information content. The fusion at the score level is fairly popular and simple because of easy availability of the scores and contains ample information to discriminate between genuine and imposter scores.
How is the choice of the fusion method determined?
The choice of the fusion method depends on the selected modalities and the distributions of their scores, the difficulty to separate the customer and the impostor classes, the amount of data available for learning and the intended application ( Ross and Jain, 2004 ).