Multi-Scenario Fusion for More Accurate Classifications of Personal Characteristics

Personal character is a stable and comprehensive description of individual human being. It consists of a multitude of characteristics, and a comprehensive personal character can provide better-personalized services. Multi-modal fusion is a popular method to classify personal characteristic. The data with different forms and structures are integrated to simulate accurate personal characteristics. However, current studies mainly focus on classifying one or few personal characteristics, whereas the majority of personal data are collected from single scenario. The problem with uni-scenario data is that it is insufficient to achieve comprehensive and accurate classification of the whole characteristics of personal character. Thusly, multi-scenario fusion of personal characteristic classification is proposed to make for such flaw. This research proposes a multi-scenario framework to illustrate the fusion process and fusion modules. The framework contains two fusion methods, namely multi-scenario feature-level fusion and multi-scenario decision-level fusion. A detailed explanation of multi-scenario fusion algorithms is provided. The objective of experiments is to verify the effect of multi-scenario fusion to realize more accurate classifications of personal characteristics as opposed to the use of uni-scenario data. Accordingly, three types of experiments were conducted, and the physiological data of 30 participants were collected for characteristic classifications. The experimental results indicate that the multi-scenario fusion overwhelmingly surpasses the use of uniscenario of data in the classification of personal characteristics.