Why do multi-stream, multi-band and multi-modal approaches work on biometric user authentication tasks?
Multi-band, multi-stream and multi-modal approaches have proven to be very successful both in experiments and in real-life applications, among which speech recognition and biometric authentication are of particular interest here. However, there is a lack of a theoretical study to justify why and how they work, when one combines the streams at the feature or classifier score levels. In this paper, we attempt to cast a light onto the latter subject. While there exists literature discussing this aspect, a study on the relationship between correlation, variance reduction and equal error rate (often used in biometric authentication) has not been treated theoretically as done here, using the mean operator. Our findings suggest that combining several experts using the mean operator, multi-layer-perceptrons and support vector machines always perform better than the average performance of the underlying experts. Furthermore, in practice, most combined experts using the methods mentioned above perform better than the best underlying expert.