Special Issue "Algorithms for Human-Computer Interaction"

A special issue of Algorithms (ISSN 1999-4893).

Deadline for manuscript submissions: 30 November 2019.

Special Issue Editor

Dr. Luigi Troiano
E-Mail Website
Guest Editor
Department of Engineering, University of Sannio, I-82100 Benevento, Italy
Interests: computational and intelligent systems

Special Issue Information

Dear Colleagues,

Human–computer interaction (HCI)| is becoming a key point in making new technologies accessible to a broad audience, taking into account cognitive processes, technical skills, cultural biases, and eventual disabilities. HCI can be effectively supported by artificial intelligence and big data. This Issue aims to explore how AI and big data can enhance the design, implementation, and validation of HCI models and tools. Algorithms invites original research papers, review articles, and case studies that are not published or being considered for publication.

Dr. Luigi Troiano
Guest Editor

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 papers will be 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. Algorithms is an international peer-reviewed open access monthly 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 1000 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.

Keywords

  • Affective computing
  • Design and evaluation of innovative interactive systems
  • Empirical studies of user behavior
  • Human language technologies and machine learning
  • Innovative interaction techniques
  • Intelligent user interfaces
  • Interface design and evaluation methodologies using AI and big data
  • User interface prototyping for interactive systems using AI and big data

Published Papers (1 paper)

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Research

Open AccessArticle
Facial Expression Recognition Based on Auxiliary Models
Algorithms 2019, 12(11), 227; https://doi.org/10.3390/a12110227 - 31 Oct 2019
Abstract
In recent years, with the development of artificial intelligence and human–computer interaction, more attention has been paid to the recognition and analysis of facial expressions. Despite much great success, there are a lot of unsatisfying problems, because facial expressions are subtle and complex. [...] Read more.
In recent years, with the development of artificial intelligence and human–computer interaction, more attention has been paid to the recognition and analysis of facial expressions. Despite much great success, there are a lot of unsatisfying problems, because facial expressions are subtle and complex. Hence, facial expression recognition is still a challenging problem. In most papers, the entire face image is often chosen as the input information. In our daily life, people can perceive other’s current emotions only by several facial components (such as eye, mouth and nose), and other areas of the face (such as hair, skin tone, ears, etc.) play a smaller role in determining one’s emotion. If the entire face image is used as the only input information, the system will produce some unnecessary information and miss some important information in the process of feature extraction. To solve the above problem, this paper proposes a method that combines multiple sub-regions and the entire face image by weighting, which can capture more important feature information that is conducive to improving the recognition accuracy. Our proposed method was evaluated based on four well-known publicly available facial expression databases: JAFFE, CK+, FER2013 and SFEW. The new method showed better performance than most state-of-the-art methods. Full article
(This article belongs to the Special Issue Algorithms for Human-Computer Interaction)
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