Self-adaptive biometric signatures based emotion recognition system
Robust and efficient emotion recognition is very important and challenging for many physiological and augmented applications. These applications however, usually suffer from the non-emotion-related varieties, the "individual varieties", especially when cope with multiple subjects. In this paper, we proposed and presented an improved grouping structure called the Self-Adaptive Biometric Signatures Based (SABSB) emotion recognition system. The system is unique in that it transforms a traditional subject-independent problem into several subjectdependent cases to remove the influence of individual varieties. Also, the selfadaptive procedure allows a flexible model generating process which is more practical for real applications. The proposed framework is implemented and tested using mixture multivariate t-distributions (mmTD). Results are compared with conventional emotion recognition system as well as the original BSB system.