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Face recognition in online soccer streaming for piracy detection.

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Title: Face recognition in online soccer streaming for piracy detection.
Authors: A. Correia, Helena1 (AUTHOR) hacorreia@ipca.pt, Pontes, Diogo1 (AUTHOR) dpontes@ipca.pt, Henrique Brito, José1,2 (AUTHOR) jbrito@ipca.pt
Source: Multimedia Tools & Applications. Aug2025, Vol. 84 Issue 26, p30737-30756. 20p.
Subjects: Piracy (Copyright), Deep learning, Face perception, Real-time computing, Sensitivity & specificity (Statistics), Human facial recognition software, Live streaming
Abstract: This paper presents a face recognition system that is part of a global solution for online soccer piracy detection. The overall solution uses several building blocks to detect illegal sharing of live soccer broadcasts. This paper presents one of the building blocks, a face recognition system, that recognizes the faces of players that participate in the soccer match that the overall solution is trying to identify. The face recognition system detects faces in the broadcast image frames and tries to match them to a database faces of players from the clubs that participate in the game. The proposed face recognition system uses Retinaface and OpenCV to detect faces, applies Deep Learning networks Facenet128 and FaceNet512 to extract features from the detected faces, computes the cosine distance between features to evaluate the dissimilarity between faces, and compares the distance to a predefined threshold. This approach aims to maximize Precision and True Positive Rate while ensuring a False Positive Rate equal to zero, even at the cost of a lower Recall and Accuracy, and also provides results in as close to real-time as possible. The experiments show that the proposed face recognition system is able to achieve a True Positive Rate of 38.4% while ensuring a False Positive Rate of 0, which is an important aspect for the overall solution. The system is able to analyze an average of 20 frames per second. The results show the potential of this approach to identify and combat illegal broadcasts of sporting events, offering a robust approach to address the escalating issue of unauthorized audiovisual content sharing. [ABSTRACT FROM AUTHOR]
Copyright of Multimedia Tools & Applications is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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  Data: Face recognition in online soccer streaming for piracy detection.
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  Data: <searchLink fieldCode="DE" term="%22Piracy+%28Copyright%29%22">Piracy (Copyright)</searchLink><br /><searchLink fieldCode="DE" term="%22Deep+learning%22">Deep learning</searchLink><br /><searchLink fieldCode="DE" term="%22Face+perception%22">Face perception</searchLink><br /><searchLink fieldCode="DE" term="%22Real-time+computing%22">Real-time computing</searchLink><br /><searchLink fieldCode="DE" term="%22Sensitivity+%26+specificity+%28Statistics%29%22">Sensitivity & specificity (Statistics)</searchLink><br /><searchLink fieldCode="DE" term="%22Human+facial+recognition+software%22">Human facial recognition software</searchLink><br /><searchLink fieldCode="DE" term="%22Live+streaming%22">Live streaming</searchLink>
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  Data: This paper presents a face recognition system that is part of a global solution for online soccer piracy detection. The overall solution uses several building blocks to detect illegal sharing of live soccer broadcasts. This paper presents one of the building blocks, a face recognition system, that recognizes the faces of players that participate in the soccer match that the overall solution is trying to identify. The face recognition system detects faces in the broadcast image frames and tries to match them to a database faces of players from the clubs that participate in the game. The proposed face recognition system uses Retinaface and OpenCV to detect faces, applies Deep Learning networks Facenet128 and FaceNet512 to extract features from the detected faces, computes the cosine distance between features to evaluate the dissimilarity between faces, and compares the distance to a predefined threshold. This approach aims to maximize Precision and True Positive Rate while ensuring a False Positive Rate equal to zero, even at the cost of a lower Recall and Accuracy, and also provides results in as close to real-time as possible. The experiments show that the proposed face recognition system is able to achieve a True Positive Rate of 38.4% while ensuring a False Positive Rate of 0, which is an important aspect for the overall solution. The system is able to analyze an average of 20 frames per second. The results show the potential of this approach to identify and combat illegal broadcasts of sporting events, offering a robust approach to address the escalating issue of unauthorized audiovisual content sharing. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Multimedia Tools & Applications is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1007/s11042-024-20389-3
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        Text: English
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        StartPage: 30737
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      – SubjectFull: Piracy (Copyright)
        Type: general
      – SubjectFull: Deep learning
        Type: general
      – SubjectFull: Face perception
        Type: general
      – SubjectFull: Real-time computing
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      – SubjectFull: Sensitivity & specificity (Statistics)
        Type: general
      – SubjectFull: Human facial recognition software
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      – SubjectFull: Live streaming
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      – TitleFull: Face recognition in online soccer streaming for piracy detection.
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              M: 08
              Text: Aug2025
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              Y: 2025
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