1,720,970 research outputs found
Automatic emotion recognition from facial expressions when wearing a mask
No full textPeople communicate emotions through several nonverbal channels and facial expressions play an important part in this communicative process. Automatic Facial Expression Recognition (FER) is a very hot topic that has attracted a lot of interest in the last years. Most FER systems try to recognize emotions from the entire face of a person. Unfortunately, due to pandemic situation, people wear a mask most of the time, thus their faces are not fully visible. In our study, we investigate the effectiveness of a FER system in recognizing emotions only from the eyes region, which is the sole visible region when wearing a mask by comparing the results of the same approach when applied to the entire face. The proposed pipeline involves several steps: detecting a face in an image, detecting a mask on a face, extracting the eyes region, and recognize the emotion expressed on the basis of such region. As it was expected, emotions that are related mainly to the mouth region (e.g. disgust) are not recognized at all and positive emotions are the ones that are better determined by considering only the region of the eyes
Affective states recognition through touch dynamics
No full textThis work exploits Touch Dynamics to recognize affective states of a user while using a mobile device. To the aim, the acquired touch pattern is segmented in swipes, successively a wide set of handcrafted features is computed to characterize the swipe. The affective analysis is obtained through machine learning techniques. Data have been collected developing a specific App designed to acquire common unlock Android touch patterns. In this way the user interaction has been preserved as the more natural and neutral possible in real environments. Affective state labels have been obtained adopting a well-known psychological questionnaire. Three affective states have been considered: anxiety, stress and depression. Tests, performed on 115 users, reported an overall accuracy of 73.6% thus demonstrating the viability of the proposed approach
Socially Inclusive Robots: Learning Culture-Related Gestures by Playing with Pepper
No full textSocial robots are being used successfully as educational technologies. In this paper we propose to use a social robot, Pepper in this case, to teach and explain the meaning of culture-related gestures to unaccompanied minor migrants and support their integration in a new culture. The use of social humanoid robots seems to be an adequate interaction mean to this aim, since they can provide both examples of gesture executions, explanations about the meaning and the context in which the gesture should be used. Moreover, as in other assistive domains, social robots may be used to attract the children's attention and support the social operator in establishing contact with these children that very often, after the difficulties of the journey, do not trust adults. Results of a preliminary study, even if performed with a limited number of participants, show that the proposed approach is suitable for learning gestures
Social Robots to Support Gestural Development in Children with Autism Spectrum Disorder
No full textChildren with Autism Spectrum Disorders (ASD) are characterized by impairments in communication and social skills, including problems in understanding and producing gestures. Using the approach of robot-based imitation games, in this paper, we propose the prototype of an imitation game that aims at improving the non-verbal communication skills, gestures in particular, of children with ASD. Starting from an application that we developed in another domain, social inclusion of migrant children, we use a social robot to teach them to recognize and produce social gestures through an imitation game. For allowing the recognition of gestures by the robot, we learned a LSTM-based model using MediaPipe for the analysis of hands positions and landmarks. The model was trained on six selected gestures for recognizing their pattern. The module is then used by the robot in the game. Results from the software accuracy point of view are encouraging and show that the proposed approach is suitable for the purpose of showing and recognizing predefined gestures, however we are aware that in the wild with ASD children it might not work properly. For this reason, in the near future, we will perform a study aiming at assessing the efficacy of the approach with ASD children and revise the model and the game accordingly
A comparative study on soft biometric approaches to be used in retail stores
No full textSoft biometric analysis aims at recognizing personal traits that provide some information about the individual. In this paper, we implemented and compared several approaches for soft biometric analysis in order to analyze humans soft biometric traits: age, gender, presence of eyeglasses and beard. Convolutional Neural Netoworks can be successfully used to understand soft biometric traits of passers-by looking at public displays and at shop windows
Detecting Emotions During Cognitive Stimulation Training with the Pepper Robot
No full textRecently, social robots are being used in therapeutic interventions for elderly people affected by cognitive impairments. In this paper, we report the results of a study aiming at exploring the affective reactions of seniors during the cognitive stimulation therapy performed using a social robot. To this purpose an experimental study was performed with a group of 8 participants in a 3-weeks program in which the group was trained on specific memory tasks with the support of the Pepper robot. To assess and monitor the results, each session was video-recorded for human and automatic analyses. Given that aging causes many changes in facial shape and appearance, we detected emotions by means of a model specifically trained for recognizing facial expressions of elderly people. After testing the model accuracy and analyzing the differences with the human annotation, we used it to analyze automatically the collected videos. Results show that the model was able to detect a low number of neutral emotions and a high number of negative emotions. However, seniors showed also positive emotions during the various sessions and, while these were much higher than negative ones in the human annotation, this difference was smaller in the automatic detection. These results encourage the development of a module to adapt the interaction and the tasks to the user’s reactions in real time. In both cases, some correlations emerged showing that seniors with a lower level of cognitive impairment experienced fewer positive emotions than seniors with a more severe impairment measured with the Mini–Mental State Examination (MMSE). In our opinion, this could be due to the need for personalized cognitive stimulation therapy according to the senior’s MMSE thus providing more stimulating tasks. However, a deeper investigation should be conducted to confirm this hypothesis
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Recognizing Cognitive Emotions in E-Learning Environment
No full textIn the present work, we describe the development of a Facial Expressions Recognition (FER) system able to recognize cognitive emotions in a distance education context. In this case, many research works show that the recognition of basic emotions is not enough and that recognizing emotions more related to the presence/lack of engagement and flow would be more appropriate. Therefore, we developed a FER system able to classify the following cognitive emotions: enthusiasm, interest, surprise, boredom, perplexity, frustration, and the neutral one. After several experiments, we tested which was the best combination of features and the best algorithm for our classification problem. Results show that the combination of Action Units and gaze and a Multiclass Support Vector Machine achieves the best accuracy on the dataset. Results are encouraging and we plan to integrate the system into an e-learning platform to create a more personalized educational environment
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