Methodologies of Audio-Visual Biometric Performance Evaluation for the H2020 SpeechXRays Project

被引:0
|
作者
Mtibaa, Aymen [1 ,2 ]
Hmani, Mohamed Amine [1 ]
Petrovska-Delacretaz, Dijana [1 ]
Boudy, Jerome [1 ]
Ben Hamida, Ahmed [2 ]
Bauzou, Claude [5 ]
Crucianu, Iacob [6 ]
Markopoulos, Ioannis [7 ]
Spanakis, Emmanouil [3 ]
Nicolin, Alexandru [4 ]
Narr, Christian [8 ]
Kockmann, Marcel [8 ]
Perez, Javier [8 ]
机构
[1] Inst Polytech Paris, Telecom SudParis, Paris, France
[2] Sfax Univ, Ecole Natl Ingenieurs Sfax, ATMS, Sfax, Tunisia
[3] Fdn Res & Technol Hellas, Inst Comp, Sci, Athens, Greece
[4] Horia Hulubei Natl Inst Phys & Nucl Engn, Magurele, Romania
[5] IDEMIA, Courbevoie, France
[6] SIVECO, Bucharest, Romania
[7] FORTHNET, Athens, Greece
[8] LumenVox, Berlin, Germany
关键词
Audio-visual recognition; performance evaluation;
D O I
10.1109/atsip49331.2020.9231680
中图分类号
TP39 [计算机的应用];
学科分类号
081203 ; 0835 ;
摘要
Biometric recognition is nowadays widely used in different services and applications, making the user authentication easier and more secure than the traditional authentication system. Starting from this idea, the EU SpeechXRays project H2020 developed and evaluated in real-life environments a user recognition platform based on face and voice modalities. Since the proposed biometric solution was evaluated in real-life environments where biometric data recorded was not accessible because of the General Data Protection Regulation GDPR, the ground truth of the conducted evaluation was not available. To correctly report the performance evaluation, some methodologies were proposed to detect the errors caused by the absence of ground truth. This paper describes the biometric solution provided by the project and presents the biometric performance evaluation carried out in three real-life use case pilots on more than 2 000 users.
引用
收藏
页数:6
相关论文
共 50 条
  • [21] Comparing Learning Methodologies for Self-Supervised Audio-Visual Representation Learning
    Terbouche, Hacene
    Schoneveld, Liam
    Benson, Oisin
    Othmani, Alice
    IEEE ACCESS, 2022, 10 : 41622 - 41638
  • [22] Effect of Audio-Visual Factors in the Evaluation of Crowd Noise
    Yang, Xiaoyin
    Kang, Jian
    APPLIED SCIENCES-BASEL, 2023, 13 (06):
  • [23] Evaluation of the quality of audio-visual aided teaching of English
    Qin, Mengyang
    Qin, Mengyang (yq62me@163.com), 2020, CRL Publishing (28): : 137 - 145
  • [24] Instantaneous Evaluation of the Sense of Presence in Audio-Visual Content
    Ozawa, Kenji
    Tsukahara, Shota
    Kinoshita, Yuichiro
    Morise, Masanori
    IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS, 2015, E98D (01) : 49 - 57
  • [25] Effect of audio-visual source misalignment on timing performance
    Cass, John
    van der Burg, Erik
    Baldson, Tarryn
    PERCEPTION, 2016, 45 : 153 - 153
  • [26] KELLERMAN: EXPANDED A Live Audio-Visual Performance in the Whitsundays
    Cooke, Grayson
    SHIMA-THE INTERNATIONAL JOURNAL OF RESEARCH INTO ISLAND CULTURES, 2012, 6 (01): : 147 - 155
  • [27] PERCEPTUAL EVALUATION ON AUDIO-VISUAL DATASET OF 360 CONTENT
    Fela, Randy F.
    Pastor, Andreas
    Le Callet, Patrick
    Zacharov, Nick
    Vigier, Toinon
    Forchhammer, Soren
    2022 IEEE INTERNATIONAL CONFERENCE ON MULTIMEDIA AND EXPO WORKSHOPS (IEEE ICMEW 2022), 2022,
  • [28] REVIVE: An Audio-Visual Performance with Musical and Visual Artificial Intelligence Agents
    Tatar, Kivanc
    Pasquier, Philippe
    Siu, Remy
    CHI 2018: EXTENDED ABSTRACTS OF THE 2018 CHI CONFERENCE ON HUMAN FACTORS IN COMPUTING SYSTEMS, 2018,
  • [29] Optimal weighting of bimodal biometric information with specific application to audio-visual person identification
    Hu, Roland
    Damper, R. I.
    INFORMATION FUSION, 2009, 10 (02) : 172 - 182
  • [30] Big Data Analytics for Smart Cities: The H2020 CLASS Project
    Quinones, Eduardo
    Bertogna, Marko
    Hadad, Erez
    Ferrer, Ana Juan
    Chiantore, Luca
    Reboa, Alfredo
    SYSTOR'18: PROCEEDINGS OF THE 11TH ACM INTERNATIONAL SYSTEMS AND STORAGE CONFERENCE, 2018, : 130 - 130