Fusion of multiple experts in multimodal biometric personal identity verification systems

We investigate two trainable methods of classifier fusion in the context of multimodal personal identity verification involving eight experts which exploit voice characteristics and frontal face biometrics. As baseline classifier combination methods, simple fusion rules (Sum and Vote) which do not require any training are used. The results of experiments on the XM2VTS database show that all four combination methods investigated yield improved performance. Trainable fusion strategies do not appear to offer better performance than simple rules.