Face detection hindering

Panteleimon Chriskos; Jonathan Munro; Vasileios Mygdalis; Ioannis Pitas

doi:10.1109/GlobalSIP.2017.8308673

Face detection hindering

Panteleimon Chriskos, Jonathan Munro, Vasileios Mygdalis, Ioannis Pitas

Research output: Chapter in Book/Report/Conference proceeding › Conference Contribution (Conference Proceeding)

6 Citations (Scopus)

334 Downloads (Pure)

Abstract

In this paper, we develop a face detection hindering method, as a means of preventing the threats to people's privacy, automatic video analysis may pose. Face detection in images or videos is the first step in human-centered video analysis to be followed, e.g. by automatic face recognition. Therefore, by hindering face detection, we also render automatic face recognition improbable. To this end, we examine the application of two methods. First, we consider a naive approach, i.e., we simply use additive or impulsive noise to the input image, until the point where the face cannot be automatically detected anymore. Second, we examine the application of the SVD-DID face de-identification method. Our experimental results denote that both methods attain high face detection failure rates.

Original language	English
Title of host publication	2017 IEEE Global Conference on Signal and Information Processing (GlobalSIP 2017)
Subtitle of host publication	Proceedings of a meeting held 14-16 November 2017, Montreal, Quebec, Canada
Publisher	Institute of Electrical and Electronics Engineers (IEEE)
Pages	403-407
Number of pages	5
ISBN (Electronic)	9781509059904
ISBN (Print)	9781509059911
DOIs	https://doi.org/10.1109/GlobalSIP.2017.8308673
Publication status	Published - Apr 2018
Event	5th IEEE Global Conference on Signal and Information Processing, GlobalSIP 2017 - Montreal, Canada Duration: 14 Nov 2017 → 16 Nov 2017

Conference

Conference	5th IEEE Global Conference on Signal and Information Processing, GlobalSIP 2017
Country/Territory	Canada
City	Montreal
Period	14/11/17 → 16/11/17

Keywords

face de-identification
face detection
privacy protection
surveillance

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10.1109/GlobalSIP.2017.8308673

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Accepted author manuscript, 1.64 MBLicence: Other

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title = "Face detection hindering",

abstract = "In this paper, we develop a face detection hindering method, as a means of preventing the threats to people's privacy, automatic video analysis may pose. Face detection in images or videos is the first step in human-centered video analysis to be followed, e.g. by automatic face recognition. Therefore, by hindering face detection, we also render automatic face recognition improbable. To this end, we examine the application of two methods. First, we consider a naive approach, i.e., we simply use additive or impulsive noise to the input image, until the point where the face cannot be automatically detected anymore. Second, we examine the application of the SVD-DID face de-identification method. Our experimental results denote that both methods attain high face detection failure rates.",

keywords = "face de-identification, face detection, privacy protection, surveillance",

author = "Panteleimon Chriskos and Jonathan Munro and Vasileios Mygdalis and Ioannis Pitas",

year = "2018",

month = apr,

doi = "10.1109/GlobalSIP.2017.8308673",

language = "English",

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pages = "403--407",

booktitle = "2017 IEEE Global Conference on Signal and Information Processing (GlobalSIP 2017)",

publisher = "Institute of Electrical and Electronics Engineers (IEEE)",

address = "United States",

note = "5th IEEE Global Conference on Signal and Information Processing, GlobalSIP 2017 ; Conference date: 14-11-2017 Through 16-11-2017",

}

Chriskos, P, Munro, J, Mygdalis, V & Pitas, I 2018, Face detection hindering. in 2017 IEEE Global Conference on Signal and Information Processing (GlobalSIP 2017): Proceedings of a meeting held 14-16 November 2017, Montreal, Quebec, Canada. Institute of Electrical and Electronics Engineers (IEEE), pp. 403-407, 5th IEEE Global Conference on Signal and Information Processing, GlobalSIP 2017, Montreal, Canada, 14/11/17. https://doi.org/10.1109/GlobalSIP.2017.8308673

Face detection hindering. / Chriskos, Panteleimon; Munro, Jonathan; Mygdalis, Vasileios et al.
2017 IEEE Global Conference on Signal and Information Processing (GlobalSIP 2017): Proceedings of a meeting held 14-16 November 2017, Montreal, Quebec, Canada. Institute of Electrical and Electronics Engineers (IEEE), 2018. p. 403-407.

Research output: Chapter in Book/Report/Conference proceeding › Conference Contribution (Conference Proceeding)

TY - GEN

T1 - Face detection hindering

AU - Chriskos, Panteleimon

AU - Munro, Jonathan

AU - Mygdalis, Vasileios

AU - Pitas, Ioannis

PY - 2018/4

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N2 - In this paper, we develop a face detection hindering method, as a means of preventing the threats to people's privacy, automatic video analysis may pose. Face detection in images or videos is the first step in human-centered video analysis to be followed, e.g. by automatic face recognition. Therefore, by hindering face detection, we also render automatic face recognition improbable. To this end, we examine the application of two methods. First, we consider a naive approach, i.e., we simply use additive or impulsive noise to the input image, until the point where the face cannot be automatically detected anymore. Second, we examine the application of the SVD-DID face de-identification method. Our experimental results denote that both methods attain high face detection failure rates.

AB - In this paper, we develop a face detection hindering method, as a means of preventing the threats to people's privacy, automatic video analysis may pose. Face detection in images or videos is the first step in human-centered video analysis to be followed, e.g. by automatic face recognition. Therefore, by hindering face detection, we also render automatic face recognition improbable. To this end, we examine the application of two methods. First, we consider a naive approach, i.e., we simply use additive or impulsive noise to the input image, until the point where the face cannot be automatically detected anymore. Second, we examine the application of the SVD-DID face de-identification method. Our experimental results denote that both methods attain high face detection failure rates.

KW - face de-identification

KW - face detection

KW - privacy protection

KW - surveillance

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ER -

Face detection hindering

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