Multimodal biometric system is a type of biometrics system that utilizes more than one biometric modality. This can include facial recognition and fingerprint scanning, iris recognition and palm print recognition, signature verification and gait analysis, as well as speech and voice recognition. The multimodal aspect means that if one modality fails, the other can pick up the slack. This is a much more flexible approach to biometrics and can offer better resistance against spoofing than unimodal systems.
There are a few different types of multimodal biometrics systems. They differ in terms of the number of modalities used and the level at which fusion occurs. For example, a system that uses both face and iris recognition would be considered a multimodal system even if the face and iris information was captured using different imaging sensors. There are also multimodal systems that combine the outputs of multiple biometrics recognition algorithms. In these systems, the outputs are compared to each other and the best match is selected. This can be useful when the inputs are not the same type of data (e.g., one is text and the other is a video).
Most research on multimodal biometrics focuses on feature-level data fusion. This involves extracting features from each of the modalities and combining them in order to create a composite image or score that is used to identify the subject. The goal is to improve the performance of each individual biometric recognition method by reducing intermodal variance and error.
One of the main challenges with multimodal fusion is that the data must be in a compatible format. For example, it is difficult to combine data from different sensors that have different resolutions. In addition, there may be differences in the way the biometrics are measured or collected. This is why the quality of the data is so important.
Another challenge is that the fusion method must be robust. This is because there is always the possibility that hackers could steal a database of biometrics. This is why a good fusion technique should be able to resist attack from spoofing attacks and from data that has been corrupted or altered. It should also be able to work under varying environmental conditions. This is why a number of methods have been developed that are intended to make fusion more stable and reliable.