Emotion Recognition by Using Bimodal Fusion

In order to improve the single-mode emotion recognition rate, the bimodal fusion method based on speech and facial expression was proposed. Here emotion recognition rate can be defined as ratio of number of images properly recognized to the number of input images. Single mode emotion recognition term can be used either for emotion recognition through speech or through facial expression. To increase the rate w e combine these two methods by using bimodal fusion. To do the emotion detection through facial expression we use adaptive sub layer compensation ( ASLC) based facial edge detection method and for emotion detection through speech we use well known SVM . Then bimodal emotion detection is obtained by using probability analysis

Music-aided affective interaction between human and service robot

This study proposes a music-aided framework for affective interaction of service robots with humans. The framework consists of three systems, respectively, for perception, memory, and expression on the basis of the human brain mechanism. We propose a novel approach to identify human emotions in the perception system. The conventional approaches use speech and facial expressions as representative bimodal indicators for emotion recognition. But, our approach uses the mood of music as a supplementary indicator to more correctly determine emotions along with speech and facial expressions. For multimodal emotion recognition, we propose an effective decision criterion using records of bimodal recognition results relevant to the musical mood. The memory and expression systems also utilize musical data to provide natural and affective reactions to human emotions. For evaluation of our approach, we simulated the proposed human-robot interaction with a service robot, iRobiQ. Our perception system exhibited superior performance over the conventional approach, and most human participants noted favorable reactions toward the music-aided affective interaction.open0

Audiovisual integration of emotional signals from others' social interactions

Audiovisual perception of emotions has been typically examined using displays of a solitary character (e.g., the face-voice and/or body-sound of one actor). However, in real life humans often face more complex multisensory social situations, involving more than one person. Here we ask if the audiovisual facilitation in emotion recognition previously found in simpler social situations extends to more complex and ecological situations. Stimuli consisting of the biological motion and voice of two interacting agents were used in two experiments. In Experiment 1, participants were presented with visual, auditory, auditory filtered/noisy, and audiovisual congruent and incongruent clips. We asked participants to judge whether the two agents were interacting happily or angrily. In Experiment 2, another group of participants repeated the same task, as in Experiment 1, while trying to ignore either the visual or the auditory information. The findings from both experiments indicate that when the reliability of the auditory cue was decreased participants weighted more the visual cue in their emotional judgments. This in turn translated in increased emotion recognition accuracy for the multisensory condition. Our findings thus point to a common mechanism of multisensory integration of emotional signals irrespective of social stimulus complexity