Abstract
A biometric is one of the common types of pattern recognition, which acquires biometric data from a person. From these data, a feature is established and extracted where these features can be used to identify individual. Exiting works in biometric Identification concentrate on unimodal biometric identification. As such, using features that are uniquely belonging to a person would decrease fraud possibility. Hence, owing to their great accurateness, multimodal biometric systems have become more favored compared with unimodal biometric systems in identification. However, these systems are highly complex. We proposed Mean-Discrete feature-based fusion algorithm for person detection. Its viability and advantage over the unimodal biometric systems are highlighted.
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