Towards multimodal emotion recognition
Multimedia indexing is about developing techniques allowing people to effectively find media. Content-based methods become necessary when dealing with large databases as people cannot possibly annotate all available content. Emotions are intrinsic in human beings and are known to be very important for natural interactions, decision making, memory, and many other cognitive functions. Current technologies allows exploring the emotional space by mean of content-based analysis of audio and video, but also thanks to other modalities such as the human physiology.In this paper, we present the latest development in the emotion recognition part of SAMMI [18] by mean of an extensive study on feature selection and the application of many of the principles we have presented in [17] and [15]. Then, we present the concepts of output thresholding, inverse thresholding, and profiling which we used for improving the results of the recognition. Finally, we present a study on the robustness to rotations and zoom of our feature point tracking system.Our experiments on the six prototypical emotions by Ekman and Friesen presented in the eNTERFACE'05 database result in as much as 75% correct recognition rate.