A Study of Multimodal Colearning, Application in Biometrics and Authentication

"Multimodality" refers to utilizing multiple communication methods to comprehend our environment better and enhance the user's experience. Using multimodal data, we may provide a complete picture of an event or object by including new information and perspectives. Improvements in single-mode apps' performance have been possible thanks to developments in deep learning algorithms, computational infrastructure, and massive data sets. Using many modalities is superior to using a single modality, according to research dating back to 2009. The study explains the limitations of single biometric-based methods in providing security and efficiency. The multimodal architecture is based on different forms of data, such as video, audio, images, and text. Combining these kinds of data is utilized to help people learn and imitate. We provide discussions on various methods to fuse different modalities of data. Recent studies have shown that cutting-edge deep-learning techniques can give even better results in multimodal biometrics and authentication systems on mobile devices. The chapter explains different problems in multimodal colearning, various multimodal fusion methods, existing challenges, and future directions.