An affective mood booster robot based on emotional processing unit

Lui, Jui Hua; Samani, Hooman; Tien, Kun-yu

doi:10.1109/cacs.2017.8284239

Cited by 16 publications

(2 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…As an effort to improve the accuracy when the information is taken from the wild (as for social robots in service), an emerging strategy consists of considering multimodal or multisource approaches. Thus, a few works have started to adopt multimodal approaches combining several modalities based on the information captured by several robots’ sensors, such as: (i) from Kinect cameras to recognise emotion based on human facial expression and gait, as the study presented in [ 46 ]; (ii) from cameras and the speech system of robots, some studies combine facial and speech [ 47 , 48 , 49 , 50 , 51 , 52 , 53 ] and body gesture and voice [ 5 ] to detect human emotions and accordingly improve HRI or navigation; (iii) from text and speech by converting speech to text to then apply Natural Language Processing (NLP) to recognise emotions, as done in [ 54 ]. However, this topic of robotics is still limited, as the survey presented in [ 55 ] reported.…”

Section: Related Workmentioning

confidence: 99%

Group Emotion Detection Based on Social Robot Perception

Quiroz

Patiño

Díaz-Amado

et al. 2022

Sensors

View full text Add to dashboard Cite

Social robotics is an emerging area that is becoming present in social spaces, by introducing autonomous social robots. Social robots offer services, perform tasks, and interact with people in such social environments, demanding more efficient and complex Human–Robot Interaction (HRI) designs. A strategy to improve HRI is to provide robots with the capacity of detecting the emotions of the people around them to plan a trajectory, modify their behaviour, and generate an appropriate interaction with people based on the analysed information. However, in social environments in which it is common to find a group of persons, new approaches are needed in order to make robots able to recognise groups of people and the emotion of the groups, which can be also associated with a scene in which the group is participating. Some existing studies are focused on detecting group cohesion and the recognition of group emotions; nevertheless, these works do not focus on performing the recognition tasks from a robocentric perspective, considering the sensory capacity of robots. In this context, a system to recognise scenes in terms of groups of people, to then detect global (prevailing) emotions in a scene, is presented. The approach proposed to visualise and recognise emotions in typical HRI is based on the face size of people recognised by the robot during its navigation (face sizes decrease when the robot moves away from a group of people). On each frame of the video stream of the visual sensor, individual emotions are recognised based on the Visual Geometry Group (VGG) neural network pre-trained to recognise faces (VGGFace); then, to detect the emotion of the frame, individual emotions are aggregated with a fusion method, and consequently, to detect global (prevalent) emotion in the scene (group of people), the emotions of its constituent frames are also aggregated. Additionally, this work proposes a strategy to create datasets with images/videos in order to validate the estimation of emotions in scenes and personal emotions. Both datasets are generated in a simulated environment based on the Robot Operating System (ROS) from videos captured by robots through their sensory capabilities. Tests are performed in two simulated environments in ROS/Gazebo: a museum and a cafeteria. Results show that the accuracy in the detection of individual emotions is 99.79% and the detection of group emotion (scene emotion) in each frame is 90.84% and 89.78% in the cafeteria and the museum scenarios, respectively.

show abstract

Section: Related Workmentioning

confidence: 99%

Group Emotion Detection Based on Social Robot Perception

Quiroz

Patiño

Díaz-Amado

et al. 2022

Sensors

View full text Add to dashboard Cite

show abstract

“…Different images can intuitively display human emotions. Traditional image feature extraction methods usually require artificial setting of image features to be extracted, such as color features [3] and texture features [4]. Although such features can accurately express the features of the image, the operation of manual feature screening is very tedious, and multiple features cannot be taken into consideration at one time.…”

Section: Introductionmentioning

confidence: 99%

Multichannel Fusion Based on modified CNN for Image Emotion Recognition

Zhao¹

2022

Journal of Compurters

View full text Add to dashboard Cite

<p>Social media networks are an integral part of people’s daily life. Users share images and texts to express their emotions and opinions. Analyzing the image and text content published by these users can help understand and predict user behavior, so as to carry out marketing, public opinion monitoring and personalized recommendation. Weibo, Wechat and other social media are important ways of self-expression. Images are more intuitive than text. Therefore, more scholars begin to pay attention to the research of image emotion analysis. At present, image emotion analysis methods pay seldom attention to the influence of saliency object and face on image emotion expression. Therefore, we propose a multichannel fusion method based on modified CNN for image emotion recognition. Firstly, saliency target and face target region are detected in the whole image. Then feature pyramid is used to improve CNN to recognize saliency target emotion. Weighted loss CNN emotion recognition is constructed on multi-layer supervision module. Finally, the saliency target emotion, face target emotion and the directly recognized emotion on the whole image are fused to get the final result of emotion classification. Experimental results show that the proposed method can improve the accuracy of image emotion recognition.</p> <p> </p>

show abstract