Dear Colleagues,

We invite submissions to a Special Issue of Electronics on the subject of “Human Affective Behavior for Quality of Experience Estimation”. The Quality of Experience (QoE) reflects the subjective quality perceived by the users and it has become vital for the successful deployment of multimedia services and applications. Although the collection of users’ subjective perceived quality and feedback is crucial to identify the root causes of quality degradation, the common utilization of interviews and self-report techniques may be subject to biases from factors not related to the stimulus, such as the interviewer’s reaction to the questions, the way the questions are formulated, and the laboratory context.

For these reasons, alternative approaches to QoE estimation can be considered, including human affective behaviors, which are driven by the human emotions naturally revealed during the user–system interaction. These emotions can be automatically inferred by analyzing human facial expressions, speech, and body gestures, and can be used to estimate the user’s perceived QoE automatically and unobtrusively without the user’s feedback. Therefore, the latest innovations in the artificial intelligence (AI) field may be relevant in terms of feature extraction and manipulation, data selection, and the creation of models and neural networks for QoE estimation.

This Special Issue includes papers that either discuss new engineering and science or review the existing literature. Potential topics include but are not limited to:

• Relationship between human affective behavior and QoE;
• Subjective studies investigating human affective behavior and QoE for multimedia services;
• Algorithms and features for the recognition of human affective behavior from face, speech, and body gestures;
• Analysis of spoken language for speech emotion recognition (SER);
• Analysis of prosody and voice quality of affective speech;
• Analysis of face for facial expression recognition (FER);
• Analysis of gestures for emotional body gesture recognition;
• Machine-learning-based QoE estimation models based on features extracted from face, speech, and/or body gestures;
• AI-driven QoE estimation of emerging multimedia services;
• Methods for the multi-modal recognition of human affective behavior;
• Human–computer Interaction systems with emotional intelligence;
• Methods to manage and optimize the QoE based on the monitored human affective behavior;
• Application of affective computing on multimedia services and QoE estimation;
• Datasets for the emotional analysis of human behavior.

Dr. Alessandro Floris
Dr. Simone Porcu
Dr. Arslan Ahmad
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Electronics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

• quality of experience
• human affective behavior
• affective computing
• facial emotion recognition
• speech emotion recognition
• emotional body gesture recognition
• artificial intelligence
• machine learning
• QoE estimation models