Special Issue "Pattern Recognition in Multimedia Signal Analysis"

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: 28 February 2021.

Special Issue Editors

Dr. Theodoros Giannakopoulos
Guest Editor
Institute of Informatics and Telecommunications, National Center for Scientific Research, Athens, Greece
Interests: audio/speech analysis; multimodal information retrieval
Prof. Dr. Evaggelos Spyrou
Guest Editor
Department of Informatics and Telecommunications, University of Thessaly, Greece
Interests: semantic multimedia analysis indexing and retrieval; feature extraction and modeling; visual context modeling; human activity recognition
Special Issues and Collections in MDPI journals

Special Issue Information

Dear Colleagues,

Huge amounts of multimedia data have been generated in recent years, either through profesional “content providers” (TV, movies, internet TV, and music videos) or user-generated content (vlogs, social media multimodal content, and multisensor data). Therefore, the need for automatic indexing, classification, content visualization, and recommendation, through multimodal pattern recognition, is obvious for various applications. In addition, multimedia data exhibit much richer structures and representations than simple forms of data and, as a result, the related pattern recognition approaches must take that into consideration.

This Special Issue focuses on novel approaches for analyzing multimodal content using pattern recognition and signal analysis algorithms. Application areas include but are not limited to video summarization, content-based multimedia indexing and retrieval, content-based recommender systems, multimodal behavior and emotion recognition, patient/elderly home monitoring based on multimodal sensors, mental health monitoring, and autonomous driving.

In this Special Issue, we invite submissions that report on cutting-edge research in the broad spectrum of pattern recognition in multimedia analysis, related to the aforementioned areas. Survey papers and reviews in a specific research and/or application area are also welcome. All submitted papers will undergo our standard peer-review procedure. Accepted papers will be published in open-access format in Applied Sciences and collected together on this Special Issue website.

Dr. Theodoros Giannakopoulos
Prof. Dr. Evaggelos Spyrou
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. Applied Sciences 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 2000 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.


  • Multimodal signal processing;
  • Multimedia pattern recognition;
  • Audio-visual fusion.

Published Papers (1 paper)

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Open AccessFeature PaperArticle
Distracted and Drowsy Driving Modeling Using Deep Physiological Representations and Multitask Learning
Appl. Sci. 2021, 11(1), 88; https://doi.org/10.3390/app11010088 - 24 Dec 2020
In this paper, we investigated various physiological indicators on their ability to identify distracted and drowsy driving. In particular, four physiological signals are being tested: blood volume pulse (BVP), respiration, skin conductance and skin temperature. Data were collected from 45 participants, under a [...] Read more.
In this paper, we investigated various physiological indicators on their ability to identify distracted and drowsy driving. In particular, four physiological signals are being tested: blood volume pulse (BVP), respiration, skin conductance and skin temperature. Data were collected from 45 participants, under a simulated driving scenario, through different times of the day and during their engagement on a variety of physical and cognitive distractors. We explore several statistical features extracted from those signals and their efficiency to discriminate between the presence or not of each of the two conditions. To that end, we evaluate three traditional classifiers (Random Forests, KNN and SVM), which have been extensively applied by the related literature and we compare their performance against a deep CNN-LSTM network that learns spatio-temporal physiological representations. In addition, we explore the potential of learning multiple conditions in parallel using a single machine learning model, and we discuss how such a problem could be formulated and what are the benefits and disadvantages of the different approaches. Overall, our findings indicate that information related to the BVP data, especially features that describe patterns with respect to the inter-beat-intervals (IBI), are highly associates with both targeted conditions. In addition, features related to the respiratory behavior of the driver can be indicative of drowsiness, while being less associated with distractions. Moreover, spatio-temporal deep methods seem to have a clear advantage against traditional classifiers on detecting both driver conditions. Our experiments show, that even though learning both conditions jointly can not compete directly to individual, task-specific CNN-LSTM models, deep multitask learning approaches have a great potential towards that end as they offer the second best performance on both tasks against all other evaluated alternatives in terms of sensitivity, specificity and the area under the receiver operating characteristic curve (AUC). Full article
(This article belongs to the Special Issue Pattern Recognition in Multimedia Signal Analysis)
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Planned Papers

The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.

Title: Face Morphing, a Modern Threat to Border Security: Recent Advances and Open Challenges

Authors: Erion Vasilis Pikoulis, Zafeiria-Marina Ioannou, Mersini Paschou, Evangelos Sakkopoulos

Abstract: Face morphing poses a serious threat to Automatic Border Control (ABC) and Face Recognition Systems (FRS) in general. Recent advances and open challenges concerning automatic detection of morphing attacks are presented in this paper. Despite the progress being made, the general consensus of the research community is that significant effort and resources are needed in the near future for the mitigation of the issue.
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